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AIR POLLUTION AND CHILD HEALTH

ADVANCE COPY

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Air pollution and child health: prescribing clean air

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Contents

Acknowledgements

Abbreviations and acronyms

Preface

Executive summary

1. Introduction

2. Routes of exposure to air pollution

2.1 Inhalation

2.2 In utero

2.3 Ingestion

3. Vulnerability and susceptibility of children

4. Sources of air pollution

4.1 Ambient air pollution

4.2 Household air pollution

4.3 Social determinants of exposure

4.4 References

5. Effects of air pollution on child health

5.1 Adverse birth outcomes

5.2 Infant mortality

5.3 Neurodevelopment

5.4 Childhood overweight and obesity

5.5 Respiratory effects

5.6 Otitis media

5.7 Childhood cancers

5.8 Later health outcomes

6. Recommended actions for health professionals

6.1 Be informed.

6.2 Recognize exposure and the associated health conditions.

6.3 Conduct research and publish and disseminate knowledge.

6.4 Prescribe solutions and educate families and communities.

6.5 Educate colleagues and students.

6.6 Advocate to policy- and decision-makers.

6.7 Benefits of cleaner air for health and the climate

6.8 A perspective on children’s health and air pollution: improving equity and access to protect

the most vulnerable

Annex 1. Global initiatives and organizations working on children’s health and air pollution

Annex 2. Additional tables and figures

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Acknowledgements

Preparation of the main document and this summary was coordinated by Marie-Noël Bruné

Drisse, Department of Public Health, Environment and Social Determinants, WHO

headquarters, Geneva, Switzerland.

WHO gratefully acknowledges the contributions of many colleagues and experts, who

dedicated time and effort and gave invaluable advice throughout the long preparation

process. The initial concept and early draft of this document were prepared with Yun-Chul

Hong and Eunhee Ha (Republic of Korea) and their teams, who dedicated several weeks to

the initial reviews. We are thankful to Lesley Brennan, Irena Buka, Fiona Goldizen, Amalia

Laborde and Peter Sly in WHO collaborating centres on children’s environmental health for

their suggestions, creative ideas and technical support throughout. We express appreciation

for the dedicated work of a team of young professional WHO interns and volunteers,

Virginia Arroyo Nebreda, Julia Gorman, Irene Martinez Morata and Paige Preston, who have

a shared passion for protecting children’s health. The document was initially reviewed in

depth by a dedicated team of experts, including Irena Buka, Francesco Forastiere, Tom

Luben and Sumi Mehta. The colleagues and experts throughout the world who are engaged

in protecting children from air pollution and who ensured that this publication would become

a reality are listed below.

Co-authors and main contributors

Heather Adair-Rohani, WHO

Virginia Arroyo Nebreda, WHO intern

Lesley J. Brennan, WHO Collaborating Centre, University of Alberta, Canada

Marie-Noël Bruné Drisse, WHO

Irena Buka, WHO Collaborating Centre, University of Alberta, Canada

Francesco Forastiere, WHO consultant

Fiona Goldizen, WHO Collaborating Centre, University of Queensland, Australia

Julia Gorman, WHO intern

Sophie Gumy, WHO

Eunhee Ha, Ewha Womans University, Republic of Korea

Yun Chul Hong, Seoul National University, Republic of Korea

Amalia Laborde, WHO Collaborating Centre, Toxicology Department, Faculty of Medicine,

Uruguay

Jessica Lewis, WHO

Tom Luben, Environmental Protection Agency, United States of America (USA)

Sumi Mehta, Vital Strategies, USA

Irene Martinez Morata, WHO intern

Pierpaolo Mudu, WHO

Paige Preston, WHO intern

Giulia Ruggeri, WHO consultant

Peter Sly, WHO Collaborating Centre for Children’s Health and Environment, University of

Queensland, Australia

Adriana Sosa, WHO Collaborating Centre, Toxicology Department, Faculty of Medicine,

Uruguay

Reviewers and technical contributors

Alan Abelsohn, World Organization of Family DoctorsMarc Aguirre, HOPE worldwide,

South Africa

Lujain Al-Qodmani, World Medical Association

A. Basel Al-Yousfi, WHO Regional Office for the Eastern Mediterranean

Kalpana Balakrishnan, WHO Collaborating Centre, Sri Ramachandra Medical College and

Research Institute, India

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Joanne Bosanquet, WHO Collaborating Centre for Public Health Nursing & Midwifery,

Public Health England, International Council of Nurses

Francesco Branca, WHO

Gloria Chen, WHO consultant

Jeanne Conry, International Federation of Gynecology and Obstetrics

Lilian Corra, International Society of Doctors for the Environment, Argentina

Sandra Cortes, School of Medicine, Advanced Centre for Chronic Diseases, Catholic

University of Chile, Chile

Bernadette Daelmans, WHO

Gregory B. Diette, Johns Hopkins University, USA

Carlos Dora, WHO

Ruth Etzel, Environmental Protection Agency, USA

Elaine Fletcher, WHO

Dongbo Fu, WHO

Guillermo Grau, Medical School, University of Buenos Aires, Argentina.

Alok Gupta, Paediatric Specialties Clinic, Mansarovar, Jaipur, India

Carisse C. Hamlet, Vital Strategies, USA

Changwoo Han, Seoul National University, Republic of Korea

Thiago Hérick de Sa, WHO

Jessica Ho, WHO

John W. Holloway, University of Southampton, England

Elizabeth Hom, University of California at San Francisco, USA

Heeji Hong, Sookmyung Women's University, Republic of Korea

Hongtai Huang, University of California at San Francisco, USA

Noreen M. Huni, Regional Psychosocial Support Initiative, South Africa

Andre Ilbawi, WHO

Bin Jalaludin, School of Public Health and Medicine, University of New South Wales,

Australia

Eun Mi Jung, Ewha Womans University, Republic of Korea

Woosung Kim, Seoul National University, Republic of Korea

John H. Knox, United Nations Special Rapporteur on Human Rights and the Environment

Abera Kumie, Addis Ababa University, Ethiopia

Philip J. Landrigan, Global Public Health Program, Boston College, USA

Seulbi Lee, Ewha Womans University, Republic of Korea

Wooseok Lee, Seoul National University, Republic of Korea

Mazen Malkawi, WHO Regional Office for the Eastern Mediterranean

Yasir Bin Nisar, WHOChristopher O Olopade, University of Chicago, USA

Eunkyo Park, Ewha Womans University, Republic of Korea

Frederica Perera, Mailman School of Public Health, Columbia University, USA

Helen Petach, United States Agency for International Development

Betzabe Butron Riveros, WHO Regional Office for the Americas

Juan Pablo Penas Rosas, WHO

Pablo Ruiz-Rudolph, Institute of Population Health, Faculty of Medicine, University of

Chile, Chile.

Florence Rusciano, WHO

Neil Schluger, Vital Strategies and Columbia University, USA

Emerson Silva, Universidade de Caxias do Sul, Brazil

Agnes Soares da Silva, WHO Regional Office for the Americas

Emiko Todaka, WHO

Juana Willumsen, WHO

Sanne de Wit, International Federation of Medical Students Associations

Tracey Woodruff, University of California at San Francisco, USA

Takashi Yorifuji, Okayama University, Japan

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwi_io3W_5TdAhWGaVAKHTk9BKYQFjAAegQIARAB&url=https%3A%2F%2Fwww.figo.org%2F&usg=AOvVaw2fZBZtIuoFaTjfUj_iO1ZU

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Other colleagues who provided data and information

Tarun Dua, WHO

Jamshid Gaziyev, Mandate of the Special Rapporteur on Human Rights and the Environment

Soo-Young Hwang, Office of the United Nations High Commissioner for Human Rights

James Kiarie, WHO

Rokho Kim, WHO Regional Office for the Western Pacific

Marina Maiero, WHO

Mercedes de Onis, WHO

Lesley Onyon, WHO Regional Office for South-East Asia

Annie Portela, WHO

Mathuros Ruchirawat, WHO Collaborating Centre, Chulabhorn Research Institute, Thailand

Joanna Tempowski, WHO

Rebekah Thomas Bosco, WHO

Jonathan Mingle rewrote sections in response to reviews, drafted the executive summary,

edited the document and made creative suggestions. Fiona Goldizen, a WHO consultant,

provided important text review and revision.

Graphic design was by L’IV.com

This publication was made possible with financial support from the Norwegian Ministry of

Foreign Affairs and the Climate and Clean Air Coalition and with the vision and support of

Dr Maria Neira, Director, Department of Public Health, Environmental and Social

Determinants of Health, WHO.

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Abbreviations and acronyms AAP ambient air pollution

ALRI acute lower respiratory infection

ASD autism spectrum disorders

BMI body mass index

CO carbon monoxide

DALY disability-adjusted life–year

FEV1 forced expiratory volume in 1 s

FVC forced vital capacity

HAP household air pollution

HIC high-income country

LMIC low- or middle-income country

LPG liquefied petroleum gas

NOx nitrogen oxides

O3 ozone

PM particulate matter

PM1 particles < 1 µm in diameter

PM2.5 particles < 2.5 µm in diameter

PM10 particles < 10 µm in diameter

SDG Sustainable Development Goal

SGA small for gestational age

SO2 sulfur dioxide

TB tuberculosis

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Preface

This report summarizes the latest scientific knowledge on the links between exposure to air pollution

and adverse health effects in children. It is intended to inform and motivate individual and collective

action by health care professionals to prevent damage to children’s health from exposure to air

pollution. Air pollution is a major environmental

health threat. Exposure to fine particles in both the

ambient environment and in the household causes

about seven million premature deaths each year (1,2).

Ambient air pollution (AAP) alone imposes enormous

costs on the global economy, amounting to more than

US$ 5 trillion in total welfare losses in 2013 (3).

This public health crisis is receiving more attention, but one critical aspect is often overlooked: how

air pollution affects children in uniquely damaging ways. Recent data released by the World Health

Organization (WHO) show that air pollution has a vast and terrible impact on child health and

survival. Globally, 93% of all children live in environments with air pollution levels above the WHO

guidelines (see Annex 2). More than one in every four deaths of children under 5 years is directly or

indirectly related to environmental risks (4). Both AAP and household air pollution (HAP) contribute

to respiratory tract infections that resulted in 543 000 deaths in children under 5 years in 2016 (1).

Although air pollution is a global problem, the burden of disease attributable to particulate matter in

air is heaviest in low- and middle-income countries (LMICs), particularly in the WHO African,

South-East Asia, Eastern Mediterranean and Western Pacific regions (1,5). LMICs in these regions –

especially the African Region – have the highest levels of exposure to HAP due to the widespread

use of polluting fuels and technologies for basic daily needs, such as cooking, heating and lighting

(6). Poverty is correlated with high exposure to environmental health risks. Poverty can also

compound the damaging health effects of air pollution, by limiting access to information, treatment

and other health care resources.

The enormous toll of disease and death revealed by these new data should result in an urgent call to

action for the global community – and especially for those in the health sector. Strong action to

reduce exposure to air pollution offers an unparalleled opportunity to protect the health of children

everywhere. Health professionals have a central role to play in this effort. Health effects experienced

early in life can increase a child’s future risk of disease and lead to lifelong consequences. A child

who is exposed to unsafe levels of pollution early in life can thus suffer a “life sentence” of illness.

Health professionals are well positioned to communicate with families, communities and decision-

makers about these and other serious risks of exposure to air pollution.

The Sustainable Development Goals (SDGs) recognize the importance of social and environmental

factors as determinants of health. All the SDGs are clearly linked to health-related targets, reflecting

the growing awareness that health, environmental and poverty alleviation are interconnected –that

ensuring healthy lives for all (SDG 3) and making cities inclusive, safe, resilient and sustainable

(SDG 11) require universal access to energy (SDG 7) and hinge upon combating climate change

(SDG 13). The launch of the 2030 Agenda for Sustainable Development offers an unparalleled

opportunity to increase action to address the environmental hazards that undermine children’s health.

Implementing evidence-based policies and health practices to protect children from air pollution will,

in turn, be essential to realizing the Sustainable Development Agenda: reducing children’s exposure

can have enormous benefits due to avoided disease, reduced mortality and improved well-being.

Reducing air pollution can also improve health and well-being by slowing climate change. It is

estimated that, by 2030, climate change will be responsible for 250 000 deaths each year (7). As

many of the same pollutants that threaten health, such as black carbon and ozone (O3), are also

important agents of atmospheric warming, interventions that reduce their emissions are likely to

result in benefits for both children’s health and the climate.

We must seize this opportunity to create healthy, sustainable environments for our children.

Everyone has a role to play, at every level: individuals, families, paediatricians, family doctors,

nurses, obstetricians and gynaecologists, primary health care providers and other community

workers, communities, medical students, national governments and international agencies. Their

efforts should be guided by the best available evidence on the health effects of air pollution on

children and on effective interventions to counter them. This document is designed to support this

effort. It reports the latest scientific knowledge on the health effects of air pollution in children. The

The evidence is clear: air

pollution has a devastating

impact on children’s health.

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breadth and depth of the evidence make clear that air pollution is a formidable disruptor of children’s

health – one that deserves far greater attention from both policy-makers and health professionals. As

children experience the consequences of air pollution in special, specific ways, they deserve to be

assessed in a special way. This publication provides practical, reliable information for health

professionals, paediatricians and other clinicians in all countries. It will be a useful reference for

action: a compendium of the accumulating evidence on the links between air pollution and children’s

health and a source of guidance for health care providers in their clinical practice and in their

collective communication of risks and solutions to the public and to policy-makers.

Children are society’s future. But they are also

its most vulnerable members. The immense

threat posed to their health by air pollution

demands that health professionals respond with

focused, urgent action. Although more rigorous

research into how air pollution affects

children’s health will continue to be valuable, there is already ample evidence to justify strong, swift

action to prevent the damage it clearly produces. Health professionals must come together to address

this threat as a priority, through collective, coordinated efforts. For the millions of children exposed

to polluted air every day, there is little time to waste and so much to be gained.

References – preface

1. Ambient air pollution: a global assessment of exposure and burden of disease, second edition. Geneva:

World Health Organization; (in press).

2. Burden of disease from the joint effects of household and ambient air pollution for 2016. Version 2 May

2018. Summary of results. Geneva: World Health Organization; 2018

(http://www.who.int/airpollution/data/cities/en/, accessed August 2018).

3. World Bank, Institute for Health Metrics and Evaluation. The cost of air pollution: strengthening the

economic case for action. Washington (DC): World Bank; 2016

(http://documents.worldbank.org/curated/en/781521473177013155/The-cost-of-air-pollution-

strengthening-the-economic-case-for-action, accessed 20 September 2018). Licence: Creative Commons

Attribution CC BY 3.0 IGO.

4. Prüss-Ustün A, Wolf J, Corvalán C, Bos R, Neira M. Preventing disease through healthy environments: a

global assessment of the burden of disease from environment risks. Geneva: World Health Organization;

2016 (http://www.who.int/iris/handle/10665/204585, accessed August 2018).

5. Exposure to ambient air pollution from particulate matter for 2016. Version 2 April 2018. Summary of

results. Geneva: World Health Organization; 2018 (http://www.who.int/airpollution/data/cities/en/,

accessed August 2018).Exposure to household air pollution for 2016. Version 5 April 2018. Summary of


accessed August 2018).

6. Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and

2050s. Geneva: World Health Organization; 2014 (http://www.who.int/iris/handle/10665/134014,

accessed September 2018).

Children are uniquely

vulnerable to the damaging

health effects of air pollution.

http://documents.worldbank.org/curated/en/781521473177013155/The-cost-of-air-pollution-strengthening-the-economic-case-for-action

http://documents.worldbank.org/curated/en/781521473177013155/The-cost-of-air-pollution-strengthening-the-economic-case-for-action

http://www.who.int/iris/handle/10665/204585

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Executive summary

Children’s exposure to air pollution

Exposure to air pollution is an overlooked health emergency for children1 around the world. While

such exposure is a persistent problem in some high-income countries (HICs) – especially in low-

income communities within those countries – the vast majority of child deaths from exposure to

particulate matter air pollution occur in LMICs.

Exposure to air pollution from particulate matter occurs both outdoors and indoors. AAP is primarily

derived from fossil fuel combustion, industrial processes, waste incineration, agricultural practices

and natural processes such as wildfires, dust storms and volcanic eruptions. The main sources of air

pollution may vary from urban to rural areas, but no area is, strictly speaking, safer. AAP was

responsible for 4.2 million premature deaths in 2016; of these, almost 300 000 were children under 5

years (1,2).

The risks associated with breathing HAP can be just as great. Breathing clean air at home is essential

for children’s healthy development, but widespread dependence on solid fuels and kerosene for

cooking, heating and lighting results in far too many children living in terribly polluted home

environments. About three billion people worldwide still depend on polluting fuels and devices for

cooking and heating (3). Women and children spend most of their time around the hearth, exposed to

smoke from cooking fires, resulting in indoor concentrations of some pollutants that are five or six

times the levels in ambient air. The widespread lack of access to clean household energy has tragic

consequences on a vast scale: HAP was responsible for 3.8 million premature deaths in 2016,

including over 400 000 deaths of children under 5 years (4).

Exposure to ambient air pollution

The proportions of children exposed to levels of fine particulate matter (PM2.5) higher than the WHO

air quality guideline levels (Fig. 1) are as follows:

• 93% of all children and about 630 million children under 5 years in the world;

• in LMICs, 98% of all children under 5 years;

• in HICs, 52% of children under 5 years;

• in the WHO African and Eastern Mediterranean regions, 100% of all children under 5 years;

• in LMICs in the South-East Asia Region, 99% of all children under 5 years;

• in LMICs in the Western Pacific Region, 98% of all children under 5 years; and

• in LMICs in the Region of the Americas, 87% of all children under 5 years.

Fig. 1. Proportions of children under 5 years living in areas in which the WHO Air Quality

Guidelines (PM2.5) are exceeded, by country, 2016

1 WHO defines a “child” as a person under 19 years of age, an “adolescent” as a person aged 10–19 years, an

“infant” as a person aged 0–11 months and a “newborn” as a person aged 0–28 days. References to “child

mortality” usually pertain to children aged 0–59 months.

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Source: (5)

Exposure to household air pollution

In 2016, 41% of the world’s population was exposed to HAP from cooking with polluting fuels and

technologies. The use of polluting fuels and technologies for cooking is almost exclusively a problem

in LMICs, as 83% of the population in the African Region, 59% in the South‐East Asia Region and

42% in the Western Pacific Region rely primarily on polluting cooking fuels. The Eastern

Mediterranean Region follows, with 31% of its population relying primarily on polluting fuels and

devices, while the proportions in the Region of the Americas and the European Region are 13% and

6%, respectively.

Children’s vulnerability and susceptibility to air pollution

Air pollution is a global public health crisis. Exposure to pollutants in the air threatens the health of

people of all ages, in every part of the world, in both urban and rural areas, but it affects the most

vulnerable among us – children – in unique ways. Children are at greater risk than adults from the

many adverse health effects of air pollution, owing to a combination of behavioural, environmental

and physiological factors. Children are uniquely vulnerable and susceptible to air pollution,

especially during fetal development and in their earliest years. Their lungs, organs and brains are still

maturing. They breathe faster than adults, taking in more air and, with it, more pollutants. Children

live closer to the ground, where some pollutants reach peak concentrations. They may spend much

time outside, playing and engaging in physical activity in potentially polluted air. New-borns and

infants, meanwhile, spend most of their time indoors, where they are more susceptible to HAP.

Children spend much time near their mothers while the latter cook with polluting fuels and devices.

Children have a longer life expectancy than adults, so latent disease mechanisms have more time to

emerge and affect their health. Their bodies, and especially their lungs, are rapidly developing and

therefore more vulnerable to inflammation and other damage caused by pollutants. In the womb, they

are vulnerable to their mothers’ exposure to pollutants. Health effects from preconception exposure

can also impose latent risks on the fetus. Even after birth, they often remain powerless to change their

environment: the very youngest cannot simply get up and walk out of a smoke-filled room. The

consequences of their exposure – through inhalation, ingestion or in utero – can lead to illness and

other health burdens that last a lifetime. But children depend entirely on us – adults – to protect them

from the threat of unsafe air.

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Children’s burden of disease related to air pollution

Tables 1 and 2 show the joint burden of disease from AAP and HAP.

• Globally in 2016, one in every eight deaths was attributable to the joint effects of AAP and HAP

– a total of 7 million deaths.

• Some 543 000 deaths in children under 5 years and 52 000 deaths in children aged 5–15 years

were attributed to the joint effects of AAP and HAP in 2016.

• Together, HAP from cooking and AAP cause more than 50% of acute lower respiratory

infections (ALRI) in children under 5 years of age in LMICs.

• Of the total deaths attributable to the joint effects of HAP and AAP worldwide in 2016, 9% were

in children.

Table 1. Death rate per 100 000 children attributable to the joint effects of HAP and AAP in

2016, by WHO region and income level

WHO region Income level Children < 5 years Children 5–14 years

African LMIC 184.1 12.9

HIC 4.3 1.4

Americas LMIC 14.2 0.7

HIC 0.3 0

South-East Asia LMIC 75 2.5

European LMIC 8.8 0.6

HIC 0.3 0

Eastern Mediterranean LMIC 98.6 3.6

HIC 5.3 0.4

Western Pacific LMIC 20.5 1

HIC 0.3 0

All LMIC 88.7 4.5

HIC 0.6 0.1

World 80.5 4.1

LMIC, low- and middle-income country; HIC, high-income country

Table 2. Population attributable fractions of child mortality due to ALRI as a joint effect of

HAP and AAP, 2016, by WHO region, the world and income level

WHO region Income level Children < 5

years (%)

Children 5–14

years (%)

African LMIC 66 66

HIC 25 24

Americas LMIC 34 34

HIC 8 7

South-East Asia LMIC 63 62

European LMIC 27 27

HIC 13 14

Eastern Mediterranean LMI 58 55

HIC 40 40

Western Pacific LMIC 53 52

HIC 12 11

All LMIC 62 62

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HIC 18 15

World 62 62

LMIC, low- and middle-income country; HIC, high-income country.

ALRI, acute lower respiratory infection; HAP, household air pollution; AAP, ambient air pollution.

Burden of disease due to AAP: In 2016, AAP was responsible for approximately 261 000 deaths

from ALRI and almost 24 million disability-adjusted life years (DALYs) among children under 5

years. The numbers of deaths from ALRI due to AAP in children under 5 years of age are shown in

Fig. 2.

The numbers of DALYs due to AAP in children under 5 years and children aged 5–14 years are

shown in Annex 2 in Fig. 15 and Fig. 16, respectively.

Fig. 2. Death rate per 100 000 per population from ALRI due to AAP in children under 5 years,

2016

Source: see Annex 2

Burden of disease due to HAP: In 2016, HAP was responsible for approximately 403 000 deaths

from ALRI and 37 million DALYs among children under 5 years (Fig. 3).

Fig. 3. Death rate per 100 000 per population from ALRI due to HAP in children under 5 years,

2016

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Source: see Annex 2

This tragically high toll is for just one disease, ALRI. The total burden of mortality and morbidity

among children due to exposure to AAP and HAP is much greater. Evidence of the many different

adverse health effects of exposure to air pollution is discussed below.

Exposure to air pollution contributes to more than half of all deaths from ALRI in children under 5

years in LMICs, making it one of the leading killers of children worldwide. The five leading causes

of death in children under 5 years globally are prematurity, acute respiratory infections, intrapartum-

related complications (including birth asphyxia), other group 1 conditions and congenital anomalies

(6). Premature birth is the only factor that kills more children under 5 years globally than acute

respiratory infections (Fig. 4). In the African Region, acute respiratory infection is the leading cause

of death of children under 5 years.

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Fig. 4. Causes of deaths among children under 5 years, 2016

Pneumonia 3% Pneumonia

13%

Intrapartum-related complications, including birth asphyxia

11% Other communicable and nutritional

conditions

10%

Neonatal sepsis 7%

Congenital anomalies and other

noncommunicable

diseases

8%

Congenital anomalies 5%

Neonatal tetanus 1%

Postneonatal (1–59 months)

Neonatal (0–27 days)

Other communicable, perinatal and nutritional conditions

3%

Injuries

6%

HIV/AIDS

1% Malaria

5%

Measles

1%

Prematurity 16%

Diarrhoea

9% Prematurity

2%

Source: Reference (6).

Several global strategies and initiatives to improve child health have included targets, policies and

interventions related to air pollution. These include the Global Action Plan for the Prevention and

Treatment of Pneumonia and Diarrhoea, the Every Newborn Action Plan and the Nurturing Care

Framework for Early Childhood Development (see Annex 1).

Sources of air pollution

Ambient air pollution: health toll, sources and solutions

AAP caused about 4.2 million premature deaths in 2016 (2). It is estimated that, in 2016, 286 000

children under 15 years of age died from exposure to unhealthy levels of AAP (see Annex 2).

Ambient air is polluted from many different sources, both anthropogenic and natural, which differ in

urban to rural areas. In urban settings, the main sources are fossil fuel combustion for energy

production, transport, residential cooking, heating and waste incineration. Rural communities in

LMICs are exposed to pollution emitted primarily from household burning of kerosene, biomass and

coal for cooking, heating and lighting, from agricultural waste incineration and from certain agro-

forestry activities (7). These processes produce complex mixtures of pollutants that can interact

chemically. They typically include carbon monoxide (CO), nitrogen oxides (NOx), lead, arsenic,

mercury, sulfur dioxide (SO2), polycyclic aromatic hydrocarbons (PAHs) and particulate matter

(PM). The last affects more people than any other air pollutant, and it is commonly used as a proxy

indicator of air pollution more broadly.

Addressing AAP is a high priority for governments and multilateral agencies around the world. Many

proven solutions are available to reduce emissions of dangerous pollutants, including cleaner

transport, cleaner cooking and heating fuels and technologies, energy-efficient housing and urban

planning, low- or zero-emission power generation, cleaner, safer industrial technologies and better

municipal waste management (8). The WHO air quality guidelines (8) provide recommended

thresholds and limits for key ambient air pollutants that must be met in order to protect health; an

updated version will be published in 2020.

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Household air pollution: health toll, sources and solutions

HAP – the single largest environmental health risk factor worldwide – is produced mainly by the

incomplete combustion of polluting fuels for cooking, heating and lighting (3, 7). In 2016, WHO

estimated that about three billion people – 41% of the world’s population –used polluting cooking

sources, most of them in LMICs (3). This number has remained largely unchanged for the past three

decades. The damage to health caused by such widespread dependence on polluting energy sources is

severe and extensive: in 2016, HAP from solid fuel use resulted in an estimated 3.8 million premature

deaths. This toll is equivalent to 6.7% of global mortality, greater than that from malaria, tuberculosis

(TB) and HIV/AIDS combined. Of these deaths, 403 000 were among children under 5 years of age

(4). HAP is also an important source of AAP, as residential cooking contributes as much as 12% of

global PM2.5 to ambient air (7).

In many parts of the world, children are especially vulnerable to HAP because they spend a great deal

of time in the home and with their mothers as the latter tend the hearth. Smoke emitted from burning

biomass, coal, charcoal and kerosene to meet the basic needs of cooking, heating and lighting is the

primary contributor to HAP (3). Burning these fuels in inefficient devices produces complex mixtures

of contaminants. In dwellings with poor ventilation, emissions of fine particulate matter and other

pollutants from stoves can reach 100 times the maximum exposure level recommended by WHO (7).

In 2014 WHO issued Guidelines for indoor air quality: household fuel combustion (9), the first

guidelines to define fuels and technologies for cooking, heating and lighting that are clean for health

at the point of use, including electricity, liquefied petroleum gas (LPG), biogas, ethanol and solar

stoves, as well as some high-performing biomass stoves. The guidelines discourage household use of

kerosene and unprocessed coal because of the serious associated health hazards. Unfortunately,

kerosene is still used for lighting by many of the about one billion people who lack access to

electricity. Achieving universal access to clean, safe household energy is a top priority on the global

sustainable development agenda, reflected in SDG 7: “ensure access to affordable, reliable,

sustainable and modern energy for all”.

Other indoor sources

Many other air pollutants that are risks to health are beyond the scope of this report. These include

volatile organic compounds from household products and building supplies, asbestos, pesticides,

mercury (e.g. from broken thermometers), radon and biological pollutants. Tobacco smoke is another

significant source of indoor air pollution and a health risk for children; the health effects of tobacco

smoke have been reviewed extensively in other WHO documents.

Social determinants of children’s health

Poverty is strongly correlated with exposure to air pollution. Children in LMICs and in low-income

communities within HICs disproportionately suffer the effects of air pollution. Poverty causes people

to rely on polluting energy sources for their basic needs, and poverty compounds the health risks

associated with their use. Poverty also limits people’s capacity to improve the environment in which

they raise their children. Air pollution is often a chronic problem in poor-quality housing and

temporary settlements. The exposure of people living in refugee camps can be particularly high, as

they are forced to scavenge for nearby wood and other fuels or to rely on kerosene stoves for heating

and cooking.

Women and girls are the primary users and procurers of household energy around the world.

Dependence on the energy sources that produce the most HAP (e.g. wood and other solid fuels) used

in inefficient stoves also poses other important health and safety risks. In many LMICs, children have

the daily or weekly task of fuel collection, often walking long distances with heavy loads of wood

and other fuels. A WHO analysis of survey data from 16 African countries in 2016 found that girls in

households that used polluting fuels and technologies spent about 18 hours each week collecting

wood or water, whereas girls in households in which clean fuels and technologies were used

primarily spent 5 hours each week in those tasks (7). This work robs children of time spent for

playing and studying. It also leads to musculoskeletal disorders and can expose children, particularly

girls, to higher risk of violence as they venture far from their household (7, 10).

16

Health effects

There is compelling evidence that exposure to air pollution damages the health of children in

numerous ways. The evidence summarized in this report is based on a scoping review of relevant

studies published within the past 10 years and input from dozens of experts around the world. It

covers adverse birth outcomes, infant mortality, neurodevelopmental disorders, childhood obesity,

lung function, ALRI, asthma, otitis media and childhood cancers.

Adverse birth outcomes. Numerous studies have shown a significant association between exposure

to AAP and adverse birth outcomes, especially exposure to PM, SO2, NOx, O3 and CO. There is

strong evidence that exposure to ambient PM is associated with low birth weight. There is also

growing evidence that maternal exposure, especially to fine PM, increases the risk of preterm birth.

There is emerging evidence for associations between exposure to air pollution and other outcomes,

such as stillbirth and infants born small for gestational age.

Infant mortality. There is compelling evidence of an association between air pollution and infant

mortality. Most studies to date have focused on acute exposure and AAP. As pollution levels

increase, so too does the risk of infant mortality, particularly from exposure to PM and toxic gases.

Neurodevelopment. A growing body of research suggests that both prenatal and postnatal exposure

to air pollution can negatively influence neurodevelopment, lead to lower cognitive test outcomes and

influence the development of behavioural disorders such as autism spectrum disorders and attention

deficit hyperactivity disorder. There is strong evidence that exposure to AAP can negatively affect

children’s mental and motor development.

Childhood obesity. A limited number of studies have identified a potential association between

exposure to AAP and certain adverse metabolic outcomes in children. The findings include positive

associations between exposure to air pollution in utero and postnatal weight gain or attained body-

mass index (BMI) for age, and an association has been reported between traffic-related air pollution

and insulin resistance in children.

Lung function. There is robust evidence that exposure to air pollution damages children’s lung

function and impedes their lung function growth, even at lower levels of exposure. Studies have

found compelling evidence that prenatal exposure to air pollution is associated with impairment of

lung development and lung function in childhood. Conversely, there is evidence that children

experience better lung function growth in areas in which ambient air quality has improved.

ALRI, including pneumonia. Numerous studies offer compelling evidence that exposure to AAP

and HAP increases the risk of ALRI in children. There is robust evidence that exposure to air

pollutants such as PM2.5, nitrogen dioxide (NO2) and O3 is associated with pneumonia and other

respiratory infections in young children. Growing evidence suggests that PM has an especially strong

effect.

Asthma. There is substantial evidence that exposure to AAP increases the risk of children for

developing asthma and that breathing pollutants exacerbates childhood asthma as well. While

relevant there are fewer studies on HAP, there is suggestive evidence that exposure to HAP from use

of polluting household fuels and technologies is associated with the development and exacerbation of

asthma in children.

Otitis media. There is clear, consistent evidence of an association between AAP exposure and the

occurrence of otitis media in children. Although relatively few studies have examined the association

between non-tobacco smoke HAP and otitis media, there is suggestive evidence that combustion-

derived HAP may increase the risk of otitis media.

Childhood cancers. There is substantial evidence that exposure to traffic-related air pollution is

associated with increased risk of childhood leukaemia. Several studies have found associations

between prenatal exposure to AAP and higher risk of retinoblastomas and leukaemia in children.

While relatively few studies have focused on HAP and cancer risk in children, HAP is strongly

associated with several types of cancer in adults and typically contains many known classified

carcinogens.

Relation between early exposure and later health outcomes. Children exposed to air pollution

prenatally and in early life is more likely to experience adverse health outcomes as they mature and

through adulthood. Exposure to air pollution early in life can impair lung development, reduce lung

17

function and increase the risk of chronic lung disease in adulthood. Evidence suggests that prenatal

exposure to air pollution can predispose individuals to cardiovascular disease later in life.

Altogether, there is clear, compelling evidence of significant associations between exposure to air

pollution and a range of adverse health outcomes. The evidence suggests that the early years, starting

in pregnancy, are the best time to invest in a child’s health, through action to improve their

environment and reduce their exposure to pollutants. This window of time offers, in effect, a great

opportunity: precisely because children are most vulnerable and sensitive to environmental influences

in their earliest years, action taken during this critical phase can yield immense health benefits.

Recommended actions for health professionals

The scientific evidence outlined above suggests many clear, concrete steps that can be taken now to

reduce the exposure of pregnant women, children and adolescents to air pollution.

Health professionals are trusted sources of information and guidance. They play an important role not

only in treating ill health caused by air pollution but also in educating families and patients about

risks and solutions and communicating with the broader public and decision-makers (Fig. 5). The role

of health professionals in the management of childhood exposure to air pollution must be amplified,

through improved methods of care and prevention and collective action. Health professionals can

provide evidence to shape public health policy and advocate for effective policies to reduce

children’s exposure to air pollution. The broader health sector must become more engaged in

developing a comprehensive approach to addressing this crisis.

Fig. 5. Critical role of health professionals

Be informed. All health professionals should consider air pollution a major risk factor for their

patients and understand the sources of environmental exposure in the communities they serve. They

should be informed about existing and emerging evidence on the ways in which air pollution may

affect children’s health.

Recognize exposure and health related conditions. Health professionals have an important role in

identifying causative risk factors in order to prevent disease. A health care provider can identify air

pollution-related risk factors by asking pertinent questions about the child’s or pregnant mother’s

environment.

Research, publish and disseminate knowledge. Health professionals can conduct research on the

effects of air pollution on children’s health. They can conduct and publish studies of the causes,

mechanisms and effects of environmental exposure of children, as well as potential treatment,

prevention and management. They can also use this evidence to inform social and behaviour change

communication strategies.

Prescribe solutions and educate families and communities. Health professionals can “prescribe”

solutions to air pollution-related problems, such as switching to clean household fuels and devices. In

contexts in which there are significant barriers to adopting clean household energy, health care

professionals can recommend “transitional” solutions that offer some incremental health benefit, and

Be informed.

Recognize exposure and related health conditions. Research, publish and disseminate knowledge. Prescribe solutions and educate families and communities. Educate colleagues and students. Advocate solutions to other sectors, policy- and decision-makers.

18

they can provide resources and information on relevant government and non-profit programmes to

help reduce exposure.

Educate colleagues and students. By training others in the health and education fields, health

professionals can increase the reach of their messages on the health risks of air pollution and

strategies to reduce exposure. Health professionals can engage their colleagues in their workplace,

local health care centres, conferences and professional associations. They can support the inclusion of

children’s environmental health in curricula in post-secondary institutions and particularly in

medical, nursing and midwifery schools.

Advocate solutions to other sectors, policy- and decision-makers. Health professionals are well

positioned to share their knowledge with decision-makers, including members of local governments

and school boards and other community leaders. Health professionals can accurately convey the

health burden of air pollution to decision-makers, conduct health-based assessments, support

improved standards and policies to reduce harmful exposures, advocate for monitoring and

emphasize the need to protect children at risk.

The need for collective action, equity and access

Low-income families have limited options to improve the air quality in their homes. Because of

market and other forces beyond their control, clean fuels and technologies may not be affordable,

available or accessible. Outside the household, individuals and families have even less control over

what is emitted into the air that surrounds them. Individual protective measures such as use of clean

stoves for cooking may mitigate HAP and improve the health of the whole family; however, reducing

AAP requires wider action, as individual protective measures are not only insufficient, but neither

sustainable nor equitable. To reduce and prevent exposure to both HAP and AAP, public policy is

essential.

Air pollutants do not recognize

political borders but travel

wherever the wind and prevailing

weather patterns take them.

Therefore, regional and

international cooperative

approaches are necessary to

achieve meaningful reductions in

children’s exposure. Approaches to

preventing exposure must be

complementary and mutually reinforcing, on every scale: houses, clinics, health care institutions,

municipalities, national governments and the global community. Health care professionals can push

together for strong action from decision-makers to protect the most vulnerable, voiceless citizens:

children who have little or no control over the air they breathe. Individual efforts can add up to

collective action that changes minds, changes policies and changes the quality of the air around us.

Such actions would go far towards ensuring that children can breathe freely, without the terrible

burdens imposed by air pollution.

References – executive summary


World Health Organization (in press).

2. Burden of disease from ambient air pollution for 2016. Version 2 May 2018. Summary of results.

Geneva: World Health Organization; 2018 (http://www.who.int/airpollution/data/en/, accessed September

2018).

3. Exposure to household air pollution for 2016. Version 5 April 2018. Summary of results. Geneva: World

Health Organization; 2018 (http://www.who.int/airpollution/data/cities/en/, accessed August 2018).

4. Burden of disease from household air pollution for 2016. Version 3 April 2018 Summary of results.

Geneva: World Health Organization; 2018. (http://www.who.int/airpollution/data/en/, accessed August

2018).

Lifting lifelong burdens: Exposure to air

pollution can alter children’s trajectory

through life, pushing them onto a path of

suffering, illness and challenge. But this is

preventable. Informed action by health

professionals can help reduce the tremendous

burden of disease in children caused by the

exposure to air pollution.

19

5. WHO’s global ambient air quality database – update 2018. Geneva: World Health Organization; 2018

(http://www.who.int/airpollution/data/cities, accessed August 2018).

6. Global Health Observatory (GHO) data. Causes of child mortality, 2016. Geneva: World Health

Organization; 2018 (http://www.who.int/gho/child_health/mortality/causes/en/, accessed August 2018).

7. Burning opportunity: clean household energy for health, sustainable development, and wellbeing of

women and children. Geneva: World Health Organization; 2016

(http://www.who.int/iris/handle/10665/204717, accessed August 2018.

8. WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global

update 2005. Summary of risk assessment. Geneva: World Health Organization; 2006

(http://www.who.int/iris/handle/10665/69477, accessed August 2018).

9. WHO guidelines for indoor air quality: household fuel combustion. Geneva: World Health Organization;


10. Inheriting a sustainable world? Atlas on children’s health and the environment. Geneva: World Health

Organization; 2017 (http://www.who.int/iris/handle/10665/254677, accessed August 2018).

http://www.who.int/airpollution/data/cities



20

1. Introduction

Every day around the world, billions of children are exposed to unsafe levels of air pollution. The

result is a global public health emergency. Air pollution, whether encountered outdoors or indoors,

poses serious risks to children’s health. In 2016, 93% of the global population under 18 years of age –

including 630 million children under 5 – were exposed to ambient levels of fine PM (PM2.5) pollution

that exceed the annual mean WHO air quality guideline (see Annex 2). About three billion people

were exposed to HAP from the use of polluting fuels for cooking in 2016 (1).

The health burden of air pollution on the world’s children is immense. Environmental factors are

responsible for an estimated 26% of all child deaths worldwide (2). ALRI are the second leading

killer of children under 5 years worldwide (3). Together, AAP and HAP cause more than 50% of all

ALRI in children under 5 in LMICs (4). In 2016, AAP and HAP were together responsible for

approximately 543 000 deaths among children under 5 from ALRI, accounting for almost 10% of all

child deaths that year (5). Over 99.9% of those deaths were in LMICs. In 2016, ALRI caused by HAP

was responsible for 441 000 deaths and around 40 million DALYs in children under 15 (6). Among

children aged 5–15 years, 52 000 deaths were

attributed to the joint effect of HAP and AAP

in 2016 (7). These deaths and lost years of

healthy life could largely be prevented by

improving the environmental conditions to

which children are exposed.

The heaviest burden on the smallest shoulders

Air pollution cuts so many lives short, but it can also lead to health burdens that last a lifetime.

Exposure increases the risks of adverse birth outcomes, neurodevelopmental disorders and reduced

lung function. In addition to respiratory infections, it is also clearly linked to a higher risk of

developing asthma, a major cause of morbidity in children.

The toll is perhaps most severe on the very youngest. Fetuses and infants have long been recognized

as especially vulnerable to the effects of exposure to environmental agents such as air pollutants, with

possible lifelong consequences (8). The earlier children’s exposure, the greater their potential loss of

healthy years of life (9). A child exposed in the first months of life can suffer lifelong effects,

including increased risks of heart disease, stroke and cancer. A growing body of evidence suggests

that air pollution can adversely affect cognitive and behavioural development in children and that

early exposure might lead to the development of chronic disease in adulthood (4). Better

understanding of the effects of air pollution on fetal and childhood growth and disease development

is critically important to inform actions and policies to protect public health.

Children live, learn and grow in various contexts and environments: the home, school, the

playground, the neighbourhood, the community, the country and the world at large. In these settings,

they encounter pollutants from a wide range of sources, with varying effects on their health. For

instance, young children in LMICs often spend much of their time with their mothers around the

home and hearth and are thus exposed to high levels of smoke emitted from cooking and heating

stoves. In poorly ventilated homes, they may breathe polluted air at levels that far exceed WHO

guidelines, while their sensitive airways, lungs and immune systems are still developing (10).

Compounding these risks is the fact that children are at the mercy of their environment, with little to

no control over their living conditions. Air pollution has not only effects on physical health effects

but can add psychological burdens of stress and anxiety (10).

Clear and mounting evidence

There is a large body of research on the effects of air pollution on child health, which is reviewed in

this report, including effects on fetal growth and birth outcomes, lung development and function,

asthma, respiratory infection and otitis media. The links between air pollution and

neurodevelopmental disorders (including autism spectrum disorder and attention deficit hyperactivity

disorder) are also reviewed, as are associations between air pollution and obesity or insulin resistance

in children (conditions that can develop into metabolic syndrome or diabetes mellitus in later life).

Air pollution causes over half of all

deaths from acute lower respiratory

infections in children under 5 in low-

and middle-income countries.

21

The report focuses on exposure to AAP and HAP from the combustion of polluting fuels. It does not

include the evidence for all sources of indoor air pollution, such as second-hand tobacco smoke,

which are beyond the scope of this document. The evidence of the harm done by tobacco smoking

and second-hand smoke to people of all ages is well established. The WHO Framework Convention

on Tobacco Control and other initiatives (e.g. the Tobacco Free Initiative) were created to reduce

exposure to tobacco smoke. The final section of this document gives suggestions for actions by

clinicians and other health care professionals to address the health effects of air pollution, some of

which include actions to reduce exposure to tobacco smoke.

Since publication of the monograph on the effects of air pollution on children’s health and

development by the WHO Regional Office for Europe in 2005 (11), many studies have been

published that strengthen the evidence of links between AAP and HAP and health effects in children.

This report summarizes the findings of the latest peer-reviewed research on a number of health

effects. As there are many studies, the report is based on systematic reviews, meta-analyses and

recent studies (published in the past 10 years).

Evidence of causal links is lacking for many exposures, as epidemiology cannot prove causation.

Action is warranted, however, when there is sufficient evidence from epidemiological studies and

experimental research that strongly suggests causality. A significant number of studies have

established associations between air pollution and various health outcomes. For outcomes for which

the evidence of links is inconsistent, “knowledge gaps” and questions for further research are

suggested.

For certain health outcomes, studies provide strong evidence of the effects of exposure to AAP, but

there are relatively few studies of the links with HAP. As the sources of AAP and HAP often overlap

significantly, the evidence for AAP could be considered indirect evidence for the health effects of

HAP. Minimizing children’s exposure to both forms of pollution, especially during the most

sensitive, developmental stages of early life, should take precedence over establishing near-certainty

about the full extent of the risk and the mechanisms involved. Preventive strategies could be based on

the evidence for AAP on the assumption that HAP has similar effects.

Informing action

Scientific understanding of the serious risks posed by air pollution early in life is robust and growing.

This knowledge must be translated into action. Taken together, the body of evidence provides ample

support for strong action and effective policy measures. The closing section of this review

accordingly suggests specific, concrete actions for paediatricians, obstetricians, health care providers,

communities and families responsible for protecting fetuses, infants and children.

Recent WHO publications on environmental risk factors for health reveal the scope of the problem.

This document builds on and adds to the evidence in those reports: the atlas on children’s health and

the environment (12), “Burning opportunity” (10), the guidelines for indoor air quality associated

with household fuel combustion (13) and the guidelines for ambient air quality (14).

Scope and purpose of the report

Environmental health – and particularly paediatric environmental health – is experiencing accelerated

growth. Health care providers who work daily with children, adolescents and their families and

communities are on the front line of assessment, treatment and prevention of environment-related

diseases and are in a key position to educate the public and provide guidance to parents of young

children on ways to mitigate the health risks from air pollution in their home environment.

This document provides health professionals with a summary of up-to-date evidence based on an

extensive literature review on the relations between air pollution and various health outcomes in

children, including the impact of exposure during pregnancy, child growth and birth outcomes, lung

development and function, asthma, respiratory infections, otitis media, neurodevelopmental disorders

and childhood cancers. The review includes not only the best available scientific evidence but also

expert input on knowledge gaps and research needs. Case studies of successful policies and cost–

effective interventions are highlighted as examples of action that could be taken at various levels. The

aim is to provide health professionals with concrete actions to protect children from the health risks

of exposure to air pollution.

22

References – introduction

1. Exposure to household air pollution for 2016. Version 5 April 2018. Summary of results. Geneva: World

Health Organization; 2018 (http://www.who.int/airpollution/data/cities/en/, accessed August 2018).

2. Prüss-Ustün A, Wolf J, Corvalán C, Bos R, Neira M. Preventing disease through healthy environments: a

global assessment of the burden of disease from environmental risks. Geneva: World Health Organization;

2016 (http://www.who.int/iris/handle/10665/204585, accessed August 2018).,

3. Global Health Observatory (GHO) data. Causes of child mortality, 2016. Geneva: World Health

Organization; 2018 (http://www.who.int/gho/child_health/mortality/causes/en/, accessed August 2018).

4. “Don’t pollute my future!” The impact of the environment on children’s health. Geneva: World Health

Organization; 2017:30 (http://www.who.int/iris/handle/10665/254678, accessed August 2018).


World Health Organization; in press.

6. Global Health Observatory (GHO) data. Household air pollution, burden of disease. Geneva: World

Health Organization; 2018 (http://apps.who.int/gho/data/node.main.139?lang=en, accessed August 2018).

7. Burden of disease from the joint effects of household and ambient air pollution for 2016. Summary of



8. Perera FP. Multiple threats to child health from fossil fuel combustion: impacts of air pollution and

climate change. Environ Health Perspect. 2017;125(2):141–8.

9. Kassebaum NJ, Arora M, Barber RM, Bhutta ZA, Brown J, Carter A, et al. Global, regional, and national

disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE),

1990–2015: a systematic analysis for the Global Burden of Disease study 2015. Lancet.

2016;388(10053):1603–58.



(http://www.who.int/iris/handle/10665/204717, accessed August 2018.

11. Effects of air pollution on children’s health and development: a review of the evidence. Copenhagen:

WHO Regional Office for Europe; 2005.


Organization; 2017 (http://ww.who.int/iris/handle/10665/254677, accessed August 2018).

13. WHO guidelines for indoor air quality: household fuel combustion. Geneva: World Health Organization;


14. WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: global



2. Routes of exposure to air pollution

Fetuses, infants and children have unique vulnerability and susceptibility to the risks of exposure to

air pollution, including subsequent development of adverse health outcomes. These heightened risks

are due to a combination of behavioural, environmental and physiological factors. (Note: detailed

information on the sources of ambient and HAP can be found in Section 4 below.)

2.1 Inhalation

Inhalation is the primary means by which air pollutants enter the human body, through the lungs and

alveoli (tiny air-filled sacs located at the end of the bronchioles in the lungs where oxygen exchanges

with carbon dioxide in the blood). During the first few years of life, the numbers of alveoli increase

rapidly, after which the volume begins to increase; this is therefore a critical window of growth,

which may be affected by inhaled pollutants (1). The lungs grow throughout childhood and

adolescence and may be exposed to many airborne pollutants that harm growth and function (1). As

children breathe at twice the rate that adults do, they inhale larger amounts of air pollutants (2).

Particles are also moved through the respiratory system faster, allowing them to reach the lungs, the

alveoli and the bloodstream more rapidly (3). In addition, children are more physically active than

adults, so that their ventilation is even greater; and they are closer to the ground, where the levels of

pollutants are often more concentrated (4). In the case of particulate pollution, the size of the particle

http://www.who.int/airpollution/data/cities/en/


http://apps.who.int/gho/data/node.main.139?lang=en


http://ww.who.int/iris/handle/10665/254677


23

determines how far into the body it penetrates and where it is deposited. Particles < 10 µm in

diameter (PM10) are typically filtered out through the nose, whereas smaller particles, such as PM2.5

(particles < 2.5 µm in diameter) can penetrate deeper and reach the lower airways. As children

typically breathe through their mouths, the nasal filtration mechanism is often bypassed, so that more

particles move into their lungs than is the case for adults (3). During pregnancy, a woman’s

ventilation rate also increases (5,6), increasing both her own exposure and that of her fetus.

Childhood exposure to air pollution by inhalation is associated with disease later in life. Exposure to

various pollutants, including black carbon, NO2, PM2.5 and PM10, is linked to the development of

asthma in children (7–9), presumably due in large part to the generation of oxidative stress and

airway inflammation (10). Research also indicates that PM may cause systemic inflammatory and

immunological responses and remodelling in the lung (11).

Other authors have found that ultrafine particles (< 0.1 µm in diameter) can cross the alveoli, enter

the bloodstream and cause cardiovascular and cerebrovascular disease (12–15). Research shows that

PM10 stimulates alveolar macrophages to release the prothrombotic cytokine IL-6, which can cause

accelerated arterial thrombosis and increase the risk of cardiovascular events (16).

Fig. 6. Smaller particles of particulate matter penetrate deeper into the lungs

Source: Reference (17)

Inhalation of CO can also have significant health consequences (Boxes 1 and 2). Once absorbed, CO

quickly binds the haemoglobin of red blood cells (which have a high affinity for CO), creating

carboxyhaemoglobin, which displaces oxygen, potentially leading to tissue hypoxia (18).

Environmental exposure to CO has been linked to cardiovascular illness and elevated levels of

carboxyhaemoglobin in adults. Evidence suggests that chronic exposure to CO may cause changes to

the structure and function of the heart that could leave it more susceptible to stress (19).

Box 1. Dangers of carbon monoxide

CO is a colourless, odourless, tasteless toxic gas produced by incomplete oxidation during the combustion of

fuels. It can be emitted from household cooking and heating systems, vehicle exhaust, industrial processes and

fires (20). CO inhalation can be deadly. Breathing is the only pathway for CO to enter the body, where it

combines with haemoglobin in the blood and reduces oxygen-carrying capacity of the blood. Children are most

susceptible to CO toxicity due to their higher metabolic rates (21). Symptoms of CO intoxication can include

headaches, irritability, dizziness, fatigue, weakness, drowsiness, nausea, vomiting, loss of consciousness, skin

pallor, dyspnoea, palpitations, confusion and irrational behaviour (21).

Low levels of CO can be found in ambient air near roads and parking areas. Common indoor sources of CO

vary across high-, low- and middle-income countries. In high-income countries, the main indoor source of CO

24

is emissions from defective appliances for cooking or heating. If they are not properly maintained, gas burners,

wood-burning fireplaces, clogged chimneys and supplementary heaters can all be potential sources of CO. High

levels of CO have also been measured in both public and residential garages. In low- and middle-income

countries, the most important sources are the burning of biomass fuels, especially in poorly ventilated kitchens,

and tobacco smoke (22).

Box 2. Case study: Carbon monoxide: an invisible threat at home

After moving into a new house, a 9-year-old child began experiencing recurring headaches and gastrointestinal

discomfort. These symptoms were followed by an episode of loss of consciousness at home. Several medical

examinations yielded no conclusive diagnosis. His teachers reported poorer school performance. Some months

later, during the winter, the whole family suffered an episode of loss of consciousness. The ultimate diagnosis

was CO intoxication. An extended study of the family’s house found the source: a wood-burning stove used for

heating during winter.

Case report by Amalia Laborde, Professor of Toxicology, University of the Republic, Uruguay

2.2 In utero

The placenta plays a crucial role in the growth and development of the fetus, providing nutrients and

oxygen and removing waste and toxicants throughout pregnancy (23–25). Because the placenta is so

important in the exchange of substances between the mother and the fetus, it is also a pathway for

exposure of the fetus when the mother is exposed to air pollution (26, 27).

Certain inhaled or ingested pollutants that are small enough to penetrate the alveolar wall, including

ultrafine PM and heavy metals, can enter the mother’s bloodstream (26,28–31). They can then cross

the placental barrier and reach the fetus, affecting growth and development by a variety of

mechanisms (29,32,33). They can cause oxidative stress, damage DNA and reduce absorption of

nutrients by the fetus (30,34). One study suggested that alteration of placental mitochondrial DNA

content may be the mediator between exposure to air pollution and low birth weight (35). Exposure

to air pollutants in utero can alter the newborn’s immune cell population and may predispose children

to allergies and asthma (29). Maternal exposure to lead has been linked to increased fetal lead levels

and adverse effects on neurodevelopment later in life (36). A recent study also shows that maternal

exposure to HAP during pregnancy leads to chronic hypoxia in the placenta; fetal development under

these conditions may be associated with adverse pregnancy outcomes (37). Inflammation is another

important mechanism, as both maternal and intrauterine inflammation have been observed in

response to air pollution, a factor that is believed to play an important role in adverse birth outcomes

and poor neurodevelopment (38,39).

The direct consequences of air pollution on maternal health present additional risks to the fetus. For

example, both AAP and HAP have been linked to hypertension in pregnancy (40–42). Hypertensive

disorders in pregnancy are a leading cause of maternal mortality worldwide (43) and are associated

with adverse birth outcomes, including preterm birth and low birth weight (44,45). Children whose

mothers experience preeclampsia during pregnancy are also at increased risk of health complications

later in life, including endocrine, nutritional, and metabolic diseases (46).

2.3 Ingestion

Air pollutants can settle on surfaces in the home, where an infant or child can ingest them. Because

pollutants can persist in the environment for some time after their release, their impact is not always

short-lived. Some substances that are released into the air, such as mercury and pesticides, can enter

the hydrological cycle as a result of atmospheric dispersion and precipitation and can then be

ingested during contact with contaminated water, food, soil or vegetation (20,47).

Breastfeeding is the best source of nutrition for infants, as it provides them with an optimal balance

of nutrients while strengthening their immune systems and forming a bond between the mother and

the child (48). There is, however, evidence that air pollutants accumulate in breast milk, resulting in

exposure of the child. Pollutants from industrial sources, such as pesticides, fossil fuels, chemical by-

products, flame retardants, heavy metals and volatile organic compounds, can enter the mother’s

circulation by inhalation or, more commonly, ingestion before being passed into breast milk (49,50).

25

For instance, PAHs have been reported at high levels in breast milk samples in the Mediterranean

(51). PAHs are classified as carcinogenic, and exposure through breastfeeding may result in adverse

developmental outcomes (52,53). Nevertheless, the advantages of breastfeeding still far outweigh any

risks from most contaminants. WHO recommends exclusive breastfeeding for up to 6 months of age,

with continued breastfeeding and appropriate complementary foods up until 2 years of age (50,54).

Exposure to air pollutants during the pre-conception period may also affect the health of both the

mother and the fetus. A recent study showed that maternal exposure to NOx and SO2 in the months

before pregnancy is associated with an increased risk for gestational diabetes mellitus, a condition

that is associated with adverse birth outcomes and risks to maternal health (55). Studies also indicate

that exposure to SO2 before pregnancy may play a role in the formation of orofacial clefts (56).

Paternal exposure is also important. A study of persistent organic pollutants indicated that exposure

of the mother and/or the father before conception resulted in lower birth weight (57).

References – routes of exposure to air pollution

1. Calogero C, Sly PD. Developmental physiology: lung function during growth and development from birth

to old age. Eur Resp. Monograph. 2010;1:1–15.

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27. Saenen ND, Vrijens K, Janssen BG, Madhloum N, Peusens M, Gyselaers W, et al. Placental nitrosative

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exposure to ambient PM2.5 during preconception and specific periods of pregnancy: the Boston birth

cohort. Environ Health Perspect. 2016;124(10):1608–15.

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3. Vulnerability and susceptibility of children

A number of studies have established that fetuses, infants and children are particularly susceptible

and vulnerable2 to air pollutants (Fig. 7) (1–9). Children breathe more rapidly than adults, because of

their higher resting metabolic rate; as a consequence, they inhale more air – and more air pollutants –

relative to their body weight (2,6,10). Children also have a larger lung surface area per kilogram of

body weight than adults (1,2). During early life, the respiratory system grows and develops rapidly,

and the lung surface area and number of alveoli increase significantly until around 5–8 years of age

(11); a higher ratio of lung surface area to volume facilitates absorption of particles. Lung growth

continues until about 20 years of age (12). Lung development trajectories are set in early life, so that

damage during the prenatal and postnatal stages is potentially irreparable (2,6,10,11,13).

Fig. 7. Fetal development and timing of air pollution risks

2 Vulnerable: exposed to the possibility of being harmed by something. Susceptible: being likely or liable to be influenced

or harmed by something.


28


During critical windows of gestation and childhood, when organ systems are developing rapidly,

children are more vulnerable to permanent damage. At birth, the immune, respiratory and central

nervous systems are immature and highly sensitive to environmental stimuli (15), and many

mechanisms have been proposed whereby exposure to air pollutants is linked to health effects in

children. Oxidative stress and inflammation are predominant and probably play an important role in

perinatal outcomes and childhood asthma (16,17). Air pollutants may also impact endothelial

function, coagulation, and maternal hemodynamic responses during pregnancy (16).

Exposure to air pollution during pregnancy, a particularly critical period of development, has been

linked to various health outcomes. Exposure before 18 weeks of gestation has been linked to

diminished development. Various pollutants are passed from mother to fetus with oxygen and

nutrients. A mother’s exposure to air pollutants during pregnancy can result in permanent damage to

the respiratory and cardiovascular systems, cognitive impairment, intrauterine growth restriction and

even compromise the development of vital organ systems (2,18–20). Exposure during this critical

period has also been linked to permanent changes in the structure of the lungs, which can have

lifelong health consequences (11,21). The timing of environmental exposure during pregnancy

determines the effect on the developing fetus: earlier exposures tend to affect development of the

airway tree and major pulmonary vessels, whereas later exposure can influence lung volume, alveoli

and pulmonary capillaries (11).

Children’s airway passages are narrower than those of adults. Thus, any irritation and subsequent

inflammation from exposure to air pollutants can result in proportionately greater airway obstruction

(2,6,10,22). Exposure to air pollutants can exacerbate existing health conditions in children and cause

additional complications (4,23). Children with respiratory or cardiovascular conditions are at

particular risk (24).

Children may also be more susceptible than adults to the effects of air pollution because of their

behaviour. Children spend their days closer to the floor than adults, and some pollutants in household

and ambient air are found in the highest concentrations in this zone, where children breathe and play

(25,26). The concentration of nitric oxide has been reported to be significantly higher at children’s

height near heavy traffic (23,27). In the home, children are often with or near their mothers as they

cook, exposing them to additional air pollutants. Infants are often unable to move away from sources

29

of air pollution, and older children may not recognize the hazards, further compounding the risks

(26). Older children spend more time outside, running, playing and breathing hard, and this increased

ventilation exposes them to larger doses of AAP (28). Infants are likely to place objects in their

mouth, placing them at risk of ingestion of air pollutants (28). Children with pica behaviour, who

compulsively put objects in their mouths, are at particular risk.

Children have a longer life expectancy than adults, rendering them more vulnerable to the potential

health effects of air pollution in yet another way (8,12,29). They have more time to manifest a disease

with a long latency period and will potentially live longer with negative health consequences (15).

Thus, the earlier their exposure, the longer the potential chronic illness or disability they will

experience. This kind of cumulative exposure to air pollution can become a life sentence, imposed

just as life is beginning.

For the same reasons, the early years, starting in pregnancy, are the best time to invest in a child’s

health by acting to improve their environment and reduce their exposure (30). This is therefore a

window of opportunity to improve their lives. Precisely because children are most vulnerable and

sensitive to environmental influences in their earliest years, action to protect them during this critical

phase can yield immense benefits.

References – vulnerability and susceptibility of children

1. Schwartz J. Air pollution and children’s health. Pedriatrics. 2004;113(4):1037–43.

2. Effects of air pollution on children’s health and development: a review of the evidence. Copenhagen:

WHO Regional Office for Europe; 2005.

3. Children’s health and the environment: a global perspective. Geneva: World Health Organization; 2005.

4. Takenoue Y, Kaneko T, Miyamae T, Mori M, Yokota S. Influence of outdoor NO2 exposure on asthma in

childhood: meta-analysis. Pediatr Int. 2012;54(6):762–9.

5. Pedersen M, Giorgis-Allemand L, Bernard C, Aguilera I, Andersen AM, Ballester F, et al. Ambient air

pollution and low birthweight: a European cohort study (ESCAPE). Lancet Resp Med. 2013;1(9):695–

704.

6. Landrigan PJ, Etzel RA. Textbook of children’s environmental health. New York City (NY): Oxford

University Press; 2014.

7. Perera FP. Multiple threats to child health from fossil fuel combustion: impacts of air pollution and

climate change. Environ Health Perspect. 2017;125(2):141–8.

8. “Don’t pollute my future!” The impact of the environment on children’s health. Geneva: World Health



Organization; 2017 (http://www.who.int/iris/handle/10665/254677., accessed August 2018).

10. Pediatric environmental health. 3rd edition. Washington DC: American Academy of Pediatrics. Council

on Environmental Health; 2012.

11. Calogero C, Sly PD. Developmental physiology: lung function during growth and development from birth

to old age. Eur Resp. Monograph. 2010;1:1–15.

12. Principles for evaluating health risks in children associated with exposure to chemicals (Environmental

Health Criteria 237). Geneva: World Health Organization; 2006.

13. Sly PD, Boner AL, Bjorksten B, Bush A, Custovic A, Eigenmann PA, et al. Early identification of atopy

in the prediction of persistent asthma in children. Lancet. 2008;372(9643):1100–6.

14. Ritz B, Wilhelm M. Air pollution impacts on infants and children. Los Angeles (CA): UCLA Institute of

the Environment; 2008 (https://www.ioes.ucla.edu/publication/air-pollution-impacts-on-infants-and-

children/, accessed August 2018).

15. Sly PD, Flack F. Susceptibility of children to environmental pollutants. Ann N Y Acad Sci.

2008;1140(1):163–83.

16. Kannan S, Misra DP, Dvonch JT, Krishnakumar, A. Exposure to airborn particulate matter and adverse

perinatal outcomes: a biologically plausible mechanistic framework for exploring potential. Environ

Health Perspect. 2006;114(11):1636-42.


linking air pollution and asthma in children. BMC Pulmon Med. 2014;14(1):1–8.

18. Vrijheid M, Martinez D, Manzanares S, Dadvand P, Schembari A, Rankin J, et al. Ambient air pollution

and risk of congenital anomalies: a systematic review and meta-analysis. Environ Health Perspect.

2011;119(5):598–606.

19. Pedersen M, Stayner L, Slama R, Sørensen M, Figueras F, Nieuwenhuijsen MJ, et al. Ambient air

pollution and pregnancy-induced hypertensive disorders: a systematic review and meta-analysis.

Hypertension. 2014;64(3):494–500.

20. Nachman RM, Mao G, Zhang X, Hong X, Chen Z, Soria CS, et al. Intrauterine inflammation and maternal

exposure to ambient PM2.5 during preconception and specific periods of pregnancy: the Boston birth

30

cohort. Environ Health Perspect. 2016;124(10):1608–15.

21. Kajekar R. Environmental factors and developmental outcomes in the lung. Pharmacol Ther.

2007;114(2):129–45.

22. Etzel RA. Indoor and outdoor air pollution: tobacco smoke, moulds and diseases in infants and children.

Int J Hyg Environ Health. 2007;210(5):611–6.

23. Gasana J, Dillikar D, Mendy A, Forno E, Ramos Vieira E. Motor vehicle air pollution and asthma in

children: a meta-analysis. Environ Res. 2012;117:36–45.

24. Dick S, Doust E, Cowie H, Ayres JG, Turner S. Associations between environmental exposures and

asthma control and exacerbations in young children: a systematic review. BMJ Open 2014;4(2):1–7.

25. Gurunathan S, Robson M, Freeman N, Buckley B, Roy A, Meyer R, et al. Accumulation of chlorpyrifos

on residential surfaces and toys accessible to children. Environ Health Perspect. 1998;106(1):9–16.

26. Miller MD, Marty MA, Arcus A, Brown J, Morry D, Sandy MS. Differences between children and adults:

implications for risk assessment at California EPA. Int J Toxicol. 2002;21(5):403–18.

27. Kenagy HS, Lin C, Wu H, Heal MR. Greater nitrogen dioxide concentrations at child versus adult

breathing heights close to urban main road kerbside. Air Qual Atmos Health. 2016;9(6):589–95.

28. Goldizen FC, Sly PD, Knibbs LD. Respiratory effects of air pollution on children. Pediatr Pulmon.

2016;51(1):94–108.

29. Sly PD, Schüepp K. Nanoparticles and children’s lungs: Is there a need for caution? Paediatr Resp Rev.

2012;13(2):71–72.

30. Engle PL, Fernald LCH, Alderman H, Behrman J, O’Gara C, Yousafzai A, et al. Strategies for reducing

inequalities and improving developmental outcomes for young children in low-income and middle-income

countries. Lancet. 2011;378:1339–53.

4. Sources of air pollution

Air pollution from particulate matter is a problem that transcends geographical and political

boundaries. It affects people in all countries, in every type of human settlement. It flows between

nations and over oceans, from outdoors to indoors to outdoors, from the upwind countryside into the

city and then on to the countryside again. Pollutants can be found equally in high, unsafe

concentrations in the most remote, rural village and on the busiest, high-traffic urban streets. Children

are threatened by this pollution in every region of the world.

HAP and AAP are strongly interconnected: the former is a major, often underestimated source of the

latter (1,2). In this section, AAP and HAP are treated separately, as they have been considered

distinct phenomena by both scientists and policy-makers. HAP consists of emissions from all

household energy, including lighting and heating. Because most of the relevant research to date has

focused on cooking and much of the data on energy use are from surveys on cooking fuel and

technology, HAP is generally perceived as a risk mostly for rural LMICs (3). While this is broadly

true, reliance on polluting household fuels also persists in many urban areas as well. For instance,

with growing awareness of the health threat posed by AAP, heating fuels are gaining attention as an

important source of air pollution, including in HICs.

4.1 Ambient air pollution

AAP is a global health crisis. The vast majority of people live in places where it is unsafe to breathe

the air. Today, 91% of the world’s population – and 93% of its children – are exposed to fine PM at

levels that exceed the WHO-recommended limit (Fig. 8 and 9) (4). The distribution of the crisis is

also becoming less equal: in more than 4300 settlements monitored by WHO, pollution levels have

improved in cities in some HICs but worsened in poorer regions (5).

Fig. 8. Annual average concentrations of ambient PM2.5 in μg/m3, 2016.

31

Source: WHO global ambient air quality database (update 2018). http://www.who.int/airpollution/data/en/

Fig. 9. PM2.5 concentrations in more than 4300 human settlements, 2010–2016.

Source: WHO global ambient air quality database (update 2018). http://www.who.int/airpollution/data/cities/en/

The map shows sites for which data were available. Few data were available for some areas, including highly polluted

settlements.

AAP caused about 4.2 million premature deaths in 2016 (6). In 2016, an estimated 286 000 children

under 15 years of age died from exposure to AAP (see Annex 2). The sources of pollution differ in

urban and rural contexts (7), the main sources in urban settings being fossil fuel combustion for

energy production, transport, residential cooking and heating (household fuel use) and waste

incineration (8). Use of polluting fuels persists in some higher-income countries; in some countries in

Europe and the Arabian Gulf, for example, coal is still used for household heating, posing a risk of

CO poisoning for both children and adults (9). The main source of pollution in rural communities in

LMICs, however, is burning of kerosene, biomass and coal for cooking, heating and lighting,

agricultural waste incineration and certain agro-forestry activities (8).

Interventions to improve the quality of the air in rural areas must target these and other major

sources, including excessive use of agrochemicals like fertilizers and pesticides, deforestation, small-

scale industries such as charcoal production and spontaneous forest fires, fog and dust storms (10,11).

Geographical and meteorological factors influence the transport and chemistry of air pollutants.

Urban areas can affect downwind rural areas, just as rural activities – such as agricultural burning –

0 10 20 30 40 50 60 70 80 90 100+

32

can affect air quality in nearby cities. For these reasons, interventions to improve air quality in any

locality require cooperation at many levels, including regional and international (1,12).

Poverty is closely associated with high exposure to air pollution. LMICs generally experience higher

levels of exposure to PM, particularly in the WHO African, South-East Asian and Western Pacific

regions (4), where the annual mean levels of PM2.5 are 5–10 times greater than the WHO guideline

limit (13). Even in regions in which PM2.5 levels are lower and closer to the WHO limit values, such

as Europe and Latin America, the levels in LMICs are almost twice those in HICs. It should be noted

that national estimates of exposure to PM2.5 are averages and that, even within HICs, low-income

communities have disproportionately higher exposure to air pollution, as they tend to be located

closer to major sources, such as industrial facilities, high-traffic roads and power plants (14). Box 3

gives an example of a successful programme to reduce HAP.

Box 3. Better stoves, better sleep – lessons from a poverty alleviation programme in Peru (15)

Exclusive use of cleaner-burning biomass stoves was linked to better sleep and alleviation of respiratory

symptoms in children in a village in Peru.

As a part of the Peruvian Government’s “Juntos” national poverty alleviation programme, residents of the small

village of Lliupapuquio, Andahuaylas, were given cleaner-burning biomass cooking stoves. These “Inkawasi”

stoves have been demonstrated to reduce PM emissions by up to 75% and wood use by 50% as compared with

traditional stoves.

Respiratory symptoms in children were assessed while they were sleeping before and after introduction of the

stoves. The study subjects were 82 children under 15 years of age who had been exposed to smoke from

biomass fuels throughout their lives. During the initial assessment, the population reported a wide prevalence of

respiratory symptoms, and more than 33% of the children reported waking during the night, daytime sleepiness

and falling asleep at school.

Statistically significant improvements in symptoms were found after introduction of the cleaner-burning stoves;

in some cases, the symptoms disappeared. The researchers concluded that exclusive use of the new stoves was

probably an important factor in the improvement in health. This study adds to evidence of the health gains

made with proper, sustained, exclusive use of clean cooking, as opposed to “fuel stacking”, in which a

household continues to use polluting energy sources for some tasks.

The sources of AAP vary from manmade to natural, from fossil fuel combustion to crop burning to

wildfires, all of which produce a complex mixture of pollutants that can interact chemically. They

usually include CO, nitrogen oxides (NO, NO2, NOx), lead, arsenic, mercury, SO2, PAHs and PM

(PM2.5, PM10, and ultrafine particulate matter) (16). Air pollution from a combination of agricultural

activities, urban emissions and atmospheric conditions contributes to annual periods of extreme air

pollution in parts of South-East Asia (2).

Certain pollutants react in the atmosphere and in high temperatures to form secondary pollutants,

such as O3. Ground-level O3 is created when pollutants such as NOx and volatile organic compounds

react with sunlight (16,17). Whereas O3 in the upper atmosphere is beneficial, as it blocks incoming

ultraviolet radiation, exposure to ground-level O3 can cause breathing problems, trigger asthma,

reduce lung function and cause various lung diseases (16,17,18).

It is difficult to measure the changing components of this complex mixture precisely. Certain

pollutants have direct toxic effects and are also used as indicators of total exposure by researchers

and national government agencies that set and enforce air quality standards. PM (PM10, PM2.5) is one

of the most commonly used markers of exposure to air pollution in general; other common indicators

are SO2, O3, NO2, CO and lead (19,20). The WHO air quality guidelines, updated in 2005 (16) were

proposed as a basis for regulatory changes to reduce emissions of pollutants and for policy to reduce

the health impacts of air pollution. The guidelines propose recommended thresholds and limits for

these key ambient air pollutants. Setting science-based air quality standards for important pollutants

is one of the most important steps that decision-makers can take to protect the health of their citizens,

including children. Governments should adopt the WHO air quality guidelines (e.g. an annual mean

threshold of 10 μg/m3 for PM2.5) or set their own stringent emissions limits. Monitoring and

identifying areas that exceed the recommended maximum air pollution levels is essential for effective

interventions to protect health. Box 4 gives examples of initiatives to help people reduce air

pollution.

33

Box 4. Digital tools to help citizens fight air pollution and improve health

Regional and global initiatives that leverage the trend of increasing digital connectivity in societies around the

world provide citizens with access to regularly updated, online data about the quality of the air where they live.

BreatheLife is an initiative of WHO and UN Environment to raise global awareness about the health risks

posed by air pollution. Evidence-based information and resources are provided to mobilize individuals and

cities to take action to clean up the air they breathe. BreatheLife’s network of participating cities is growing,

allowing urban decision-makers to demonstrate support for and share lessons about solutions to improving air

quality. On the programme’s website (www.breathelife2030.org), individuals can access updated information

on the pollution levels in their cities and countries and about the related burden of disease and human cost of air

pollution.

Country-level initiatives to make data on air quality more accessible and user-friendly include the Air Quality

Health Index in Ontario, Canada (www.airqualityontario.com), which rates air quality on a scale from 1 to 10

and is updated daily. The index indicates action people can take to protect themselves when local pollution

levels exceed the recommended limits.

Working towards solutions

Addressing AAP is an increasingly important priority for governments and multilateral agencies.

Many proven solutions are available to reduce emissions of dangerous pollutants in cities, including

cleaner transport, cleaner cooking and heating fuels and technologies, energy-efficient housing and

urban planning, low- to zero-emission power generation, cleaner, safer industrial technologies and

better municipal waste management (21).

WHO has been publishing reports on air pollution and its health effects since 1958, including work

that led to the first air quality guidelines, in the mid-1980s (22). Since the release of the current

version of the WHO air quality guidelines in 2005, more evidence has become available on the health

effects of ambient air pollutants, even at relatively low concentrations. The guidelines are therefore

being revised to reflect the latest available evidence, and an updated version with recommended

thresholds for key air pollutants will be published in 2020.

4.2 Household air pollution

Polluted air inside homes, schools, workplaces and recreation facilities – spaces where pregnant

women, mothers, infants and children spend much of their time –cause and contribute to a wide range

of negative health outcomes. Tragically, excessive air pollution can turn the very places that are

meant to shelter and nurture children into places of risk.

HAP is produced mainly by incomplete combustion of polluting fuels used for cooking, heating and

lighting and is the single largest environmental health risk factor worldwide (8). HAP is also an

important source of AAP (23). In 2016, WHO estimated that about three billion people – 41% of the

world’s population – still used polluting fuels for cooking, mostly in LMICs (Fig. 10) (24), and this

number has remained largely unchanged for the past three decades. The damage to health caused by

dependence on polluting energy sources is severe and extensive: in 2016, HAP from solid fuel use

resulted in 3.8 million premature deaths, equivalent to 6.7% of global mortality, greater than the toll

due to malaria, TB and HIV/AIDS combined. Of these deaths, 403 000 were among children under 5

years of age (8,18). The risks of children are not limited to direct exposure, as there is emerging

evidence that pregnant mothers’ exposure to high levels of HAP is linked to higher risks of adverse

birth outcomes such as low birth weight (25).

Fig. 10. Proportions of households that used polluting fuels for cooking, 2016

34


Families’ daily acts of survival can undermine the health of their own children. Smoke emitted from

burning biomass (wood, dung and crop residues), coal, charcoal and kerosene for cooking, heating

and lighting is the primary contributor to HAP; other significant sources are tobacco smoke, candles,

incense and mosquito coils (8). Burning produces complex mixtures of contaminants, the

composition of which depends on the type of fuel used and on the temperature and phase of

combustion (26–29). For instance, emissions from the combustion of coal can contain PM, CO, NOx,

SO2, benzene, PAHs, carbon and several heavy metals (30). Kerosene combustion can emit CO, NOx,

PM, SO2, formaldehyde and PAHs (31). Kerosene smoke is also extremely rich in black carbon, a

major component of fine PM and a potent climate-warming pollutant (32).

In some regions, children are especially vulnerable to HAP because they spend so much time in the

home and with their mothers as they tend the hearth. Women and girls are the main users of

household energy around the world. They also spend significant time and effort gathering,

transporting, preparing and using fuels like biomass, coal and charcoal to cook food in inefficient

stoves or open hearths and to heat their homes. In dwellings with poor ventilation, emissions of fine

PM and other pollutants from these stoves can exceed the maximum exposure recommended by

WHO by 100 times (8).

Cooking is not the only use of household energy use that poses risks to children’s health. The WHO

guidelines for household fuel combustion (31) classify kerosene as a polluting fuel and discourage its

use as a household fuel. Nevertheless, kerosene is still used for lighting by many of the around one

billion people who lack access to electricity. Kerosene lamps are often the only means of lighting

houses at night, allowing children and adolescents to study in areas without electricity. Use of

kerosene not only pollutes the air inside houses but also increases the risks for fires, burns and CO

poisoning.

Although significant numbers of urban dwellers in LMICs still use polluting fuels and devices for

cooking, heating and lighting, the vast majority of those who use polluting household energy live in

rural areas (Fig. 11) (3). Reliance on polluting fuels is especially prevalent in rural areas of the WHO

African, South-East Asian and Western Pacific regions (8). Persistent use of polluting cooking,

heating and lighting fuels by more than three billion people – and the resulting health risks – is due

largely to lack of access to clean, affordable, convenient alternatives (33). Despite recent progress in

all WHO regions in increasing access to clean fuels and technologies (Fig. 12), the number of people

who use polluting fuels did not change appreciably between 1990 and 2016 (Fig. 13) (24). Given this

trend, better coordinated and concerted action is required to meet the SDG 7 target of achieving

universal access to clean energy by 2030.

35

Fig. 11. The “energy ladder”, showing increasing cleanliness, efficiency and convenience with

increasing prosperity


The combination of fuel and technology determines whether a stove is “clean for health”; e.g., some pellet-burning stoves

achieve WHO guideline levels in laboratory testing.

Fig. 12. Population with and without access to clean technologies and fuels for cooking, by year

and by region.


Fig. 13. Trends in access to clean fuels and technologies for cooking, 2000–2016, by region

36


AFR, African Region; AMR, Region of the Americas; EMR, Eastern Mediterranean Region; SEAR, South-East Asian

Region; WPR, Western Pacific Region

HAP and AAP are interconnected: the latter can make its way indoors, and the former contributes to

poor air quality outdoors. Globally, an estimated 12% of ambient PM2.5 comes from use of solid fuel

for cooking (35). This “leakage” of indoor pollutants outdoors is responsible for almost half a million

premature deaths due to AAP (36). Reducing cross-contamination between household and ambient

air by reducing reliance on polluting fuels in both rural and urban areas throughout the world is an

urgent priority for health sector professionals and for those working in energy and sustainable

development. Accelerating the transition to clean household energy for billions of people is a

critically important means for protecting children’s health.

Working towards solutions

In 2014, WHO issued the first set of guidelines on fuels and technologies for cooking, heating and

lighting that are clean for health (31). The guidelines include recommendations on combinations of

fuel and technology that are clean for health at the point of use, including electricity, LPG, biogas,

ethanol and solar stoves, as well as high-performing biomass stoves that meet the emission rate

targets in the guidelines. The guidelines discourage household use of kerosene and unprocessed coal

because of the serious health hazards they pose.

Achieving universal access to clean, safe household energy is a high priority on the global sustainable

development agenda, reflected in SDG 7 (“ensure access to affordable, reliable, sustainable and

modern energy for all”). Box 5 presents the human rights issues involved. WHO has resources to help

health professionals and decision-makers to integrate health concerns into planning, programmes and

policies on household energy and air quality. These include the Clean Household Energy Solutions

Toolkit, which contains modules on needs assessment, standards and testing for household energy

devices, monitoring and evaluation and materials, to help the health sector in tackling HAP. (See also

Annex 1.)

Box 5. Air pollution is a child rights issue

No group is more vulnerable to environmental harm than children. Yet, a healthy environment is essential for

children to fully enjoy their right to health. The Convention on the Rights of the Child (37), the universally

ratified human rights treaty, requires States Parties to pursue full implementation of the right to health by

appropriate measures that include the provision of nutritious foods and clean drinking-water, taking into

consideration the dangers and risks of environmental pollution (Art. 24 (2) (c)). Air pollution in particular

jeopardizes children’s right to health. Countless children suffer disease and disability from air pollution, often

with lifelong effects, as it can disrupt their physical and cognitive development.

States have heightened their obligations to respect, protect and fulfil the rights of children, who are often unable

to protect themselves from environmental harm, including air pollution. The obligations include:

• ensuring that educational programmes increase children’s understanding of environmental issues and

strengthen their capacity to respond to environmental challenges;

37

• assessing the effects of proposed measures on children’s rights before the measures are taken or approved;

• collecting information on sources of environmental harm to children and making the information publicly

available and accessible;

• facilitating the participation of children in environmental decision-making and protecting them from

reprisals for their participation or otherwise expressing their views on environmental matters; and

• removing barriers to children’s access to justice for environmental harm so that can fully enjoy their

rights.

States should adopt and implement environmental standards that are consistent with the best available evidence

and relevant international health and safety standards. Thus, States should implement recommendations from

expert agencies, such as WHO, on specific measures to protect children’s health and well-being from

environmental harm. A good example of concrete guidance is the report of the United Nations Special

Rapporteur on Human Rights and the Environment, John H. Knox, on the threat posed to children’s health and

well-being in Mongolia by air pollution (38). The Rapporteur noted that, in 2017, the Committee on the Rights

of the Child expressed serious concern about the effects of air pollution on Mongolian children, particularly in

the capital, Ulaanbaatar. Each winter, the city has some of the most heavily polluted air in the world, as

residents of gers burn coal in household stoves to stay warm. Knox observed that children are at particular risk,

jeopardizing their rights to life and health.

Other sources of indoor air pollution

Many other air pollutants are also important health risks, but fall beyond the scope of this report,

which addresses primarily sources of air pollution arising from combustion. Contaminants present in

urban and rural indoor air include volatile organic compounds, asbestos, pesticides, mercury (e.g.

from broken thermometers), radon and biological pollutants. Volatile organic compounds produce

vapours readily at room temperature and are emitted by thousands of household products, including

paints, varnishes, solvents, building materials, disinfectants, personal care products, air fresheners, art

and hobby supplies and vehicles used in attached garages (20).

Tobacco smoke is a significant source of indoor air pollution and a health risk for children (39). As

many as 4000 chemicals may be present in tobacco smoke alone (40). Asbestos is a known

occupational carcinogen, and its use in residential and education buildings can contaminate indoor air

(30). Pesticides such as insecticides and antimicrobial disinfectants are often sprayed near the ground

and can persist in indoor air or settle on surfaces (41). Radon, a radioactive carcinogenic gas naturally

present in some soil and rock, can enter houses, buildings and other enclosed spaces (42-44). This

document primarily addresses sources of air pollution arising from combustion.

Various biological pollutants are present in indoor air: dust mites, droppings and urine from pests,

insects and rodents, pollen from indoor plants and outdoor air, viruses and bacteria and fungi,

including mould and mildew, or their by-products (41,44,45). Build-up of certain biological

pollutants can trigger asthma or cause allergic reactions (46,47), and others are linked to the spread of

infectious diseases (45).

Working toward solutions

WHO has published a series of indoor air quality guidelines (Table 3) (22). There are many solutions

to reducing exposure to other air pollutants. In general, indoor environments can be made healthier

by a few key actions: avoiding tobacco smoking, improving ventilation and reducing humidity in

houses (to reduce mould and mites), storing chemicals safely and avoiding the use of unnecessary

chemicals and pesticides. Asbestos and lead paint should no longer be used in building or renovating

houses (21,43,48,49).

Table 3. Common air pollutants: sources of exposure and WHO air quality guidelines Pollutant Common sources of exposure. WHO guideline valuesa Reference

Benzene

Ambient: Building materials,

furniture, attached garages. Motor

No safe level of exposure is recommended.

43

38

vehicle exhaust. Refineries and

petrol stations.

Unit risk of leukaemia per 1 μg/m3 air

concentration is 6 × 10-6.

The concentrations of airborne benzene

associated with excess lifetime risks of

1/10 000, 1/100 000 and 1/1 000 000 are

17, 1.7 and 0.17 μg/m3, respectively.

Household: Heating and cooking

with kerosene. Activities such as

cleaning, painting, mosquito

repellents, photocopying and

printing. Tobacco smoke.

Carbon

monoxide

(CO)

Ambient: Incomplete combustion

from burning charcoal or biomass

and burning fossil fuels in motor

vehicles, electric generators and

other machinery.

100 mg/m3 – 15-min

35 mg/m3 – 1-h

10 mg/m3– 8-h

7mg/m3 – 24-h

43

Household: Heating and cooking.

Tobacco smoke. Vehicle

exhausted from attached garages.

Electric generators. Incense

burning.

Emission rates from household

fuel combustion should not

exceed

CO (unvented) 0.16 (g/min)

CO (vented) 0.59 (g/min)

31

Lead Vehicle and industry emissions,

waste incineration, natural

processes (e.g. volcanic

eruptions)

Mercury Vehicle and industry emissions,

waste incineration, natural

processes (e.g. volcanic

eruptions).

Naphthalene

Ambient air

0.01 mg/m3 – annual average 43

Crystalline (pure) naphthalene

moth repellents and disinfectants,

herbicides, charcoal lighters and

hair sprays, unvented kerosene

heaters, tobacco smoke, rubber

materials

Wood smoke, fuel oil and

gasoline.

Nitrogen

dioxide (NO2)

Ambient: Combustion processes

(heating, power generation, and

engines in vehicles and ships).

200 μg/m3 – 1-h average

40 μg/m3 – annual average

43


– gas, wood, oil, kerosene and

coal; tobacco smoke

Outdoor air. Occupational use of

vehicles indoors.

Ozone (O3) Vehicle and industry emissions,

solvents.

100 μg/m3 – 8-h mean 16

Particulate

matter (PM)

Ambient: Motor vehicle

emissions. Combustion of fossil

fuels and solid fuels. Dust.

Various sources.

PM2.5

10 μg/m3 – annual mean

25 μg/m3 – 24-h mean

PM10

20 μg/m3 – annual mean


16, 50

Household: Combustion of solid

fuels in open fires or traditional

stoves.

Kerosene.

Emission rates of PM2.5 from household

fuel combustion should not

exceed

0.23 mg/min – unvented

0.80 mg/min – vented

31

Polycyclic

aromatic

hydrocarbons

(PAH)

Ambient: Motor vehicles.

Burning of coal and oil for

electricity and industrial use.

Incomplete combustion of

No threshold can be determined, and all

indoor exposures are considered

deleterious to health.

43

39

organic materials. Unit risk for lung cancer estimated to be

8.7 × 10–5 per ng/m3 of benzo(a)pyrene.

The corresponding concentrations from

lifetime exposure to benzo(a)pyrene that

result in excess lifetime cancer risks of

1/10 000, 1/100 000 and 1/1 000 000 are

approximately 1.2, 0.12 and 0.012 ng/m3,

respectively.


with dung, wood, agricultural

residues, coal. Tobacco smoke.

Incense and candles.

Sulfur dioxide

(SO2)

Ambient: Industrial activities,

power generation, motor vehicles.


500 μg/m3 – 10-min mean

16

Household: Burning of fossil

fuels (coal and oil).

Volatile organic

compounds

Ambient: Petrochemical solvents,

vaporization of unburnt fuel,

pesticides. Combustion processes

and vehicle exhaust.

Household: Cooking, solvents,

building materials, household

products indoors at room

temperature.

No guidelines, although there are

recommendations for certain volatile

organic compounds, such as benzene,

which is carcinogenic to humans; there is

no safe threshold for exposure to benzene.

20

This list is of important pollutants in indoor air; it is not exhaustive. a Some countries and states within countries (e.g. California in the USA) have adopted guideline levels lower than those of

WHO.

4.3 Social determinants of exposure and health

Social determinants of health play a central role in the effects of HAP and AAP on health. The

circumstances in which we live powerfully influence our lives, beginning at conception (51). Social

inequalities negatively affect infant health and are associated with increased rates of infant mortality

rates (52). Studies suggest that social status, especially poverty, influences the risk of environmental

exposure. Thus, less affluent populations are at greater risk of a variety of exposures; for example,

the combined effects of socioeconomic inequality and reduced air quality can contribute to increased

infant mortality (53). It has been estimated that, in 2010, 2.7–3.4 million preterm births globally were

associated with exposure to PM2.5 during gestation (53).

Poverty and pollution are closely linked. Poverty may force people to rely on polluting fuels for their

basic needs, and poverty compounds the health risks associated with their use. Poverty also limits

people’s choices and their ability to improve the environment in which they raise their children; for

example, low-income families cannot just decide to move away from a heavily polluting industrial

site. Air pollution is often a chronic problem in poor-quality housing and temporary settlements. The

exposure of people living in refugee camps can be particularly high, as they have to scavenge for

wood and other fuels or rely on

kerosene stoves for heating and

cooking.

Dependence on the energy sources

that produce most HAP – solid fuels

such as wood – also contributes to

other important health risks. Children,

often at the expense of their schooling

or playtime, are sometimes given the

tasks of cooking on inefficient stoves

or gathering fuel. Fuel collection obliges them to walk long distances with heavy loads. This work

can lead to musculoskeletal disorders and can put children, particularly girls, at higher risk of violent

attack, rape or injury as they venture far from the household (8,34). The risk of attack while gathering

fuel is especially high for girls living in refugee camps (54,55). An analysis of survey data in 16

African countries in 2016 showed that girls in households in which polluting fuels were used spent

about 18 hours each week collecting wood or water, whereas girls in households in which clean fuels

were used primarily spent 5 hours each week in such tasks (8).

Poverty and pollution are closely

linked. The poor often have higher

exposure to air pollution and more

limited access to treatment and

interventions.

40

Gender also determines exposure of children to HAP and AAP. As they move through childhood,

their exposure to HAP and AAP may change according to their gender. In some cultures, girls are

kept in the kitchen with their mother for longer than boys because of social and cultural attitudes

towards the role of women (56). Girls are therefore more exposed to HAP and boys potentially more

exposed to AAP (57). This obviously varies, as some parents have a social preference for male

children and spending more time looking after them, so that they have greater exposure to HAP (58).

The role of gender in exposure to HAP and AAP is complex and depends on social and cultural

attitudes.

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et al. Infant mortality and air pollution: modifying effect by social class. J Occup Environ Med.

2004;46(12):1210–6.

53. Malley CS, Kuylenstierna J, Vallack HW, Henze DK, Blencowe H, Ashmore MR. Preterm birth

associated with maternal fine particulate matter exposure: a global, regional and national

assessment. Environ Int. 2017;101:173–82.

54. Beyond firewood: fuel alternatives and protection strategies for displaced women and girls. New York

City (NY): Women’s Commission for Refugee Women and Children; 2006.

55. Statistical snapshot: access to improved cookstoves and fuels and its impact on women’s safety in crises.

Factsheet. Washington DC: Global Alliance for Clean Cookstoves; 2015

(http://cleancookstoves.org/resources/353.html, accessed August 2018).

56. Armstrong JR, Campbell H. Indoor air pollution exposure and lower respiratory infections in young

Gambian children. Int J Epidemiol. 1991;20(2):424–9.

57. Matinga MN. We grow up with it: an ethnographic study of the experiences, perceptions and responses to

the health impacts of energy acquisition and use in rural South Africa. Thesis. Enschede: University of

Twente; 2010 (doc.utwente.nl/75414/1/ thesis_M_Matinga.pdf, accessed August 2018).

58. Mishra V, Smith KR, Retherford RD. Effects of cooking smoke and environmental tobacco smoke on

acute respiratory infections in young Indian children. Popul Environ. 2005;26(5):375–96.

5. Effects of air pollution on child health

Since publication of the monograph on the effects of air pollution on children’s health and

development by the WHO Regional Office for Europe in 2005 (1), further evidence has been

published of the links between AAP and HAP and the health of children.

This document provides a summary of the latest and best available science, to inform and aid

healthcare professionals’ efforts to protect children’s health. For each of the 10 sections on health

effects below, two experts did an initial scoping review and reviewed the available evidence; because

of the extensive relevant published literature, they gave priority to systematic reviews, meta-analyses

and recent studies, mainly those published within the past 10 years. A second in-depth review was

conducted to identify additional studies, with a round of extensive peer review by a geographically

diverse group of experts in air pollution and child health. The document also cites WHO publications

on air pollution that are relevant to child health. It should be noted that this document is neither a full

systematic review nor a set of official guidelines, and, because of constraints of space and time, the

review does not cover every potential effect of air pollution on children’s health.

The health effects discussed are adverse birth outcomes, infant mortality, effects on

neurodevelopment, childhood obesity, effects on lung function, acute lower respiratory infection,

asthma, otitis media and childhood cancers, demonstrating the diverse range of impacts that air

pollution can have on children, often with long-lasting consequences.

5.1 Adverse birth outcomes

Key findings:

• Numerous studies show significant associations between exposure to AAP and adverse birth

outcomes, especially in association with the pollutants PM, SO2, NOx, O3 and CO.

• A growing body of evidence shows that air pollution, particularly PM2.5, is associated with low

birth weight, and AAP is associated with preterm birth.

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• Few studies have examined the role of HAP, but there is moderate evidence of an association

between solid fuel combustion and low birth weight.

The health impacts of exposure to air pollution during the prenatal period are often overlooked but

can be quite significant. A growing body of research provides evidence of an association between

maternal exposure to air pollution and adverse birth outcomes, including stillbirth, preterm birth, low

birth weight and being small for gestational age (SGA) (2–19). Exposure before conception can also

affect the fetus, with emerging evidence of a link between exposure to air pollution from traffic and

other sources of AAP and reproductive disease in women. A systematic literature review provides

suggestive evidence of associations between exposure to AAP and the incidence of reproductive and

gynaecological diseases, including infertility and endometriosis, although the number of studies to

date is limited (20).

WHO defines stillbirth as fetal death occurring at a birth weight of ≥ 1000 g or at ≥ 28 completed

weeks of gestation (21). In 2015, an estimated 2.6 million infants were stillborn, and 98% of the

deaths occurred in LMICs (22). Preterm birth is defined by WHO as infants born alive before 37

weeks of gestation; an estimated 15 million infants are born preterm worldwide each year (7). Health

complications resulting from preterm birth are the leading cause of death among children < 5 years of

age, resulting in over one million deaths in 2016 (7, 23, 24).

Low birth weight is a major public health issue worldwide and is associated with a range of short-

and long-term health effects. Low birth weight is defined by WHO as a weight at birth of < 2500 g

(2). It has been estimated that 15–20% of all infants born worldwide have a low birth weight,

corresponding to more than 20 million births per year (3). SGA is commonly defined as having a

weight below the 10th percentile of the recommended sex-specific birth weight for gestational age

(10). SGA and low birth weight are also associated with preterm birth (18). Thus, preterm birth and

SGA can occur independently or together and can result in low birth weight (18, 25). These outcomes

have been associated with increased risks for premature death and disability, including cardiovascular

morbidity, chronic lung disease, obesity and metabolic syndrome (18, 26). There is also emerging

evidence suggestive of an increased risk for developmental delays and poorer cognitive performance

(11, 18). Children who had adverse birth outcomes may require more health care after birth, placing

demands on health facilities and resources, with wider social impacts (18).

The evidence for links between air pollution and stillbirth, preterm birth, low birth weight and SGA is

discussed below.

Stillbirth

Ambient air pollution

Several studies have been published on the effect of AAP, particularly PM2.5, and stillbirth. In a meta-

analysis (27), one study on the effect of PM2.5 on risk of stillbirth found a statistically nonsignificant

increase in risk of stillbirth per 10 μg/m3 increase in PM2.5 (28). Green and colleagues identified a

small, statistically significant increase in risk per 10 µg/m3 increase in PM2.5 in a birth cohort in

California, USA (29). Other studies reported modest increased risks associated with exposure to

PM2.5 throughout pregnancy (30) or at specific stages, such as the third trimester (31) or the week

before delivery (32). Ebisu and colleagues (33) recommended that the chemical components of PM2.5

and the specific cause of stillbirths (fetal growth, maternal complications) be determined to ensure

accurate results.

Other studies have reported increased risks of stillbirth with exposure to total AAP (34), PM2.5 (30),

PM10 (30, 35, 36), CO (28, 30), SO2 (28, 30, 36), NO2 (28–30) and O3 (29, 37). Some publications

reported nonsignificant or no associations (38, 39), and some were limited by distance at which air

pollution monitors were located from birth address or by failure to adjust for change of address

during pregnancy (28). The strongest effect for PM10, SO2, CO, and O3 in most studies was in the

third trimester (29, 30, 35, 36, 40). As for PM2.5, evidence indicates that exposure to NO2 throughout

pregnancy increases the risk of stillbirth (29, 30).

Acute exposure to AAP in the week before delivery has been the subject of relatively few studies.

New evidence suggests that such exposure increases the risk of stillbirth. Faiz and colleagues (32)

found an increased risk of stillbirth when mothers were exposed to high levels of CO, SO2, NO2 or

44

PM2.5 in the 6 days before delivery. In a retrospective cohort study of 223 375 births in the USA,

exposure to O3 during the week before delivery increased the risk of stillbirth by 13–22% (37).

Household air pollution

Pope et al. (41) identified four studies of the relation between HAP from solid fuel use and stillbirth:

three in India (42–44) and one in Pakistan (45). Three of the studies found a significant association

between exposure to HAP and increased risk of stillbirth; the fourth found an increased risk, which

was not statistically significant. The review (41) found an overall 51% increase in risk of stillbirth

with exposure to HAP. The studies differed in design, particularly with respect to the method for

exposure assessment. Two of the studies considered kerosene to be a “low pollution” fuel (43, 44),

and one considered that exposure to kerosene led to medium exposure to HAP (45), which

contradicts current understanding of the adverse health effects of kerosene. WHO guidelines for

indoor air quality associated with household fuel combustion (46) discourage use of kerosene in the

home. The review of health effects for the guidelines concluded that the findings of these four studies

were consistent and that a causal association was possible but could not be confirmed, given the

small number of studies. A more recent review (47) included an additional study and found a 29%

increase in the risk of stillbirth but found that kerosene was categorized inconsistently in the studies,

with one categorizing it as resulting in “high pollution” (48) and two as “low pollution” (43, 49); in

two studies, information on kerosene use was not collected. Different classification of kerosene may

affect the interpretation of these results and also the findings of studies on other health effects.

Preterm birth


Maternal exposure to individual air pollutants during pregnancy has been linked with preterm birth

(8, 9, 14). A number of studies have found a positive association between maternal exposure to PM2.5

and preterm birth (50–52), and it has even been estimated that, in 2010, 2.7–3.4 million preterm

births globally were associated with exposure to PM2.5 during gestation (53). One review found a

nonsignificant association between exposure to PM10 and preterm birth (52). Preterm birth has been

consistently associated with SO2 levels (54, 55), while the evidence for associations with CO, NO,

NO2 and O3 remains inconclusive (50, 53).

It is important to distinguish between very early and later preterm birth (e.g. before 26 weeks, 26–32

weeks and after 32 weeks), because each has different effects on health and probably different causes.

Many studies do not distinguish between very early and later preterm birth, although this distinction

would be useful, particularly for evaluating the effects of different levels of AAP.


Very few studies have been undertaken of HAP and preterm birth. A systematic review by Amegah

and colleagues (47) covered three studies (44, 49, 56) and found an increased risk of preterm birth

with household solid fuel use. One of the studies (44), conducted in India, gave an adjusted odds ratio

of 1.43 for preterm birth in houses in which solid fuel was used as compared with those in which

LPG or kerosene was used. The literature review for the WHO guidelines on indoor air quality

associated with household fuel combustion did not include an assessment of the quality of the

evidence for associations between polluting fuel use and preterm birth, because of the small number

of studies (46).

Box 6. Air pollution and congenital anomalies

Congenital anomalies, also known as birth defects, are structural or functional abnormalities that occur during

intrauterine life (57). They may be identified before birth, at birth or later in life. Congenital anomalies,

including metabolic disorders, account for an estimated 11% of global neonatal deaths, 6% of infant deaths and

lifelong morbidity (24). Examples of congenital anomalies linked to environmental factors include congenital

heart disease, limb reduction, kidney or urinary tract malformation, cleft lip and palate defects, cryptorchidism

and hypospadias. While there is evidence that certain birth defects are associated with exposure of nonsmoking

pregnant women to second-hand tobacco smoke, the results of studies of AAP and HAP have been inconsistent.

There is a growing body of literature on the relations between AAP and congenital anomalies, and associations

of varying strength have been found. A cohort study conducted in Wuhan, China (58), found a significant

association between maternal exposure to atmospheric PM2.5 during early pregnancy and congenital heart

45

defects in the offspring. This study of 105 988 live births, stillbirths and fetal deaths was based on 1-week

average concentrations from nearby air pollution monitors and showed a significant association with risk of

congenital heart defects, which increased monotonically as PM2.5 concentration increased. A study of traffic

pollutants (PM10 and benzene) in a community in northern Italy showed an association between exposure to

PM10 and birth defects (59). A systematic review of 10 epidemiological studies that included a meta-analysis of

four studies of associations between the risk of congenital anomalies and concentrations of various air

pollutants found statistically significant increased risks of coarctation of the aorta and of tetralogy of Fallot with

exposure to NO2 and SO2 (60). It also showed an association between exposure to PM10 and an increased risk of

atrial septic defects. Another systematic review and meta-analysis showed a significant association between

exposure to NO2 and coarctation of the aorta (61).

Because of the paucity of studies, there is insufficient evidence of an association between congenital anomalies

and HAP other than tobacco smoke. A population-based case–control study in Shanxi Province, China (62) on

the link between neural tube defects and HAP from coal combustion demonstrated a dose–response trend,

whereby the risk of a child having neural tube defects increased with the mother’s exposure to household coal

combustion pollutants.

Low birth weight


Evidence has emerged in the past decade that ambient PM affects birth weight (5, 6, 15–17, 63).

Several meta-analyses performed between 2012 and 2016 consistently showed positive associations

between exposure to PM2.5 during pregnancy and low birth weight and suggest that late pregnancy

may be a critically vulnerable time (50, 52, 64–66).

Individual chemical elements of PM may be involved in its toxic effect. In a large study of eight

pooled European cohorts, an increased amount of sulfur in PM2.5 was associated with an increased

risk of low birth weight (17). As the chemical components of PM differ widely by source, this may

explain some of the inconsistencies in the findings of different studies (64). In an extensive

systematic review of studies in China (55), SO2 was consistently associated with low birth weight.

There is less evidence of associations between exposure during pregnancy to PM10, PAH (benzene,

toluene, ethyl benzene, M- and p-xylene, and o-xylene) and other elements of AAP, such as CO and

NO2, and low birth weight (4, 52, 65).


Several studies have tested the association between HAP, particularly from use of solid fuels, and a

lower mean birth weight. These studies found an increased risk of low birth weight between 21% and

35% with exposure to HAP (25, 41, 47, 67, 68).

The review of health effects for the 2014 WHO guidelines led to the conclusion that there was

moderate evidence of an association between exposure to solid fuel combustion and low birth weight

(46). The review comprised seven studies, which had consistent findings (44, 49, 69–73). An earlier

review by Pope and colleagues (41) included many of the same studies from Guatemala, India,

Pakistan and Zimbabwe, all of which reported higher risks of low birth weight after maternal

exposure to solid fuel combustion in the home. The review found that maternal exposure to HAP

increased the risk of low birth weight by 38%, for an average reduction in birth weight of 96.6 g. A

recent systematic review by Amegah and colleagues (47) found a 35% increase in risk and an average

reduction in birth weight of 54 g, after adjustment for publication bias.

Box 7. Air pollution and adverse birth outcomes: new evidence from cohort studies in India

Air pollution is one of the leading risk factors for the national burden of disease in India (74). Exposure to

PM2.5 in AAP and HAP has been associated with low birth weight in many studies but in few studies in the

high-exposure settings that are common in LMICs such as India. Balakrishnan et al. (75) investigated whether

exposure to PM2.5 during pregnancy was associated with low birth weight in an integrated rural–urban, mother–

child cohort in Tamil Nadu. The researchers recruited 1285 women in the first trimester of pregnancy in

primary health care centres and urban health posts and followed them until the birth of their child to collect data

on maternal health, prenatal care, exposure to air pollution during pregnancy and the birthweight of the child.

They found that a 10 μg/m3 increase in exposure to PM2.5 during pregnancy was associated with a decrease in

birth weight of 4 g and a 2% increase in the prevalence of low birth weight (after adjustment for gestational

age, sex, maternal BMI, maternal age, history of a previous low-birth-weight child, birth order and season of

46

conception). By applying the exposure–response estimates of the median differences in PM2.5 concentration (of

~175 μg/m3) between households in which biomass and clean fuel were used, a 70 g decrease in birth weight

was estimated to be associated with solid fuel use (76). This study provided some of the first quantitative

estimates of the effects of exposure to PM2.5 in India to birth weight. It contributed evidence of this association

that is relevant for high-exposure settings in LMICs that experience the dual health burdens of AAP and HAP.

The findings indicate that maternal exposure to PM2.5 should be considered with other risk factors for low

birthweight in India. The study also provided baseline information for a new multi-country HAP intervention

trial under way in Guatemala, India, Peru and Rwanda on the effects of clean fuel (in this case, LPG) use on

maternal and child health (www.hapintrial.org).

Small for gestational age


Few studies have explored the association between exposure to air pollution and infants born small

for gestational age (SGA), defined as birth weight below – 2 standard deviations of the mean or

below the 10th percentile according to local intrauterine growth charts (77). Maternal exposure to

PM2.5 and PM10 has been associated with SGA births (15, 50). Le and colleagues (8) investigated the

association between SGA at term and exposure to SO2, CO, NO2, O3 and PM10 during the first month

and the third trimester of pregnancy. They found an association between SGA at term and exposure

to high CO and NO2 levels in the first month and with exposure to O3 and PM10 > 35 μg/m3 during

the third trimester. Additional evidence confirms the link between elevated NO2 levels and SGA. A

study of 2.5 million births in Canada between 1999 and 2008 found that exposure to NO2 during

pregnancy was significantly associated with infants born SGA at term. The association was

independent of PM2.5, and a dose–response relation was found. This information and evidence that

NO2 is a key component of traffic-related air pollution led the authors to suggest that exposure to

traffic is an important factor in adverse pregnancy outcomes such as SGA (54).

Social situation strongly affects birth outcome. A population-based study in the USA linked exposure

to both O3 and PM2.5 to SGA. The authors noted that more socially disadvantaged populations are at

greater risk of infants born SGA, particularly in the case of exposure to PM2.5 (12). Overall, there is

growing evidence of an association between SGA and ambient air pollution.


Associations between HAP and infants born SGA have been identified in several studies. In India,

infants born to women who used biomass fuels such as wood and/or dung as the primary cooking fuel

in the home during pregnancy were more likely to be SGA (44). In a study of pregnant women in

Zambia, household air monitors were used to measure exposure to PM2.5 and volatile organic

compounds during the first trimester (78). The authors found that increasing levels of pollutants were

associated with poor birth outcomes, and both PM2.5 and VOCs were associated with SGA. The

primary sources of pollution identified were biomass fuels (wood, charcoal, crop residues and cow

dung) used for cooking. Garbage burning was also common.

Biological mechanisms

It is difficult to determine the association between air pollution and adverse birth outcomes because

so many factors can influence the sensitive periods of development. Several plausible mechanisms

have been proposed, including oxidative stress, which may affect the embryo directly in its early

stages of development or induce DNA damage, pulmonary and placental inflammation, changes in

fetal blood coagulation or endothelial function, and altered maternal haemodynamic response (79).

The placenta is central to the health of the fetus, and airborne pollutants that reach the placenta may

cause significant damage. PAHs and CO can cross the placenta, triggering a number of effects (63,

80). Researchers found that maternal exposure to fine particulate matter (PM2.5) and CO in household

air during pregnancy increases the risk of fetal thrombotic vasculopathy, a disorder characterized by

clots on the fetal surface of the placenta that block vascular flow, and also stillbirth and low birth

weight (81).

Studies have identified epigenetic changes in the expression of maternal and fetal DNA in cases in

which air pollution has been indicated as a factor in preterm birth, suggesting a new mechanism of

action (82). Increased methylation of umbilical cord blood and placental DNA has been noted,

http://www.hapintrial.org/

47

although more research is needed (82). Studies in experimental animals showed that high maternal

exposure to PM2.5 during pregnancy can cause epigenetic changes that interfere with the cerebral

development of the embryo (83).

PM can also stimulate maternal inflammatory responses, reduce maternal immunity and increase the

risk of infection (84). Infection may cause intrauterine growth restriction or preterm labour (84).

Maternal health is critical to fetal health; therefore, if the mother’s respiratory health is jeopardized

by air pollutants, the transport and delivery of oxygen and nutrients to the fetus may be reduced (85).

Box 8. Clarion call: the FIGO opinion on the effects of exposure to toxic environmental chemicals on

reproductive health.

“Exposure to toxic environmental chemicals during pregnancy and breastfeeding is ubiquitous and is a threat to

healthy human reproduction.” That is the conclusion of the International Federation of Gynaecology and

Obstetrics (FIGO), the leading voice of reproductive health professionals, with member societies in 125

countries and territories. In 2015, FIGO published an opinion by an international group of obstetricians,

gynaecologists and scientists, formally endorsed or supported by 12 reproductive health professional

organizations worldwide (86), that there is “accumulating, robust evidence” for an association between

exposure to environmental chemicals, such as those in air pollution, and reproductive health. “Preventing

exposure to environmental chemicals is a priority for reproductive health professionals everywhere”, the

opinion states. FIGO recommends that health professionals advocate for policies to prevent exposure to toxic

chemicals in the environment, including air pollution.

Conclusions

Pregnancy is a highly vulnerable time. A growing body of evidence shows a link between exposure

to air pollution and adverse birth outcomes, which may have lasting health consequences. There is

robust evidence that exposure to air pollution, especially ambient PM, is associated with low birth

weight. Likewise, there is growing evidence that maternal exposure to AAP, especially to fine PM,

increases the risk of preterm birth. While the reported strength of association between stillbirth and

exposure to air pollutants (e.g. PM, CO) depends on the individual pollutant, several studies have

shown an increased risk of stillbirth linked to higher exposures. There is evidence suggestive of a link

between exposure to ambient and household air pollution and infants born SGA. While additional

research will advance knowledge, the harmful effects of air pollution on fetal development and birth

are clear, and efforts must be made to protect future generations.

Knowledge gaps and research needs

• A substantial number of studies have examined the link between air pollution and various birth

outcomes. The studies differ widely in the populations studied, the method and the levels of

exposure to air pollution.

• More studies should be conducted on the association between exposure to ultrafine PM and birth

outcomes and also on exposure to air pollution and preterm birth.

• As many studies on birth outcomes and air pollution are based on general estimates of exposure,

studies should be conducted with state-of-the-art techniques for measuring and modelling air

quality to increase the validity of the links between exposure to various air pollutants and birth

outcomes and to improve the evidence base for environmental health policies to ensure the

health of mothers and children.

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environmental chemicals. Int J Gynecol Obstet. 2015;131(3):219–25.

5.2 Infant mortality

Key findings:

• While there are few studies, those on the link between exposure to PM and infant mortality have

provided compelling evidence of a positive association globally.

• Most studies have addressed acute exposure to AAP with relatively few on the effects of HAP

on the risk of infant mortality.

Infant mortality refers to deaths that occur in the first year of life. In 2016, the global infant mortality

was 4.2 million, representing 75% of all deaths of children under the age of 5 years (1). While many

studies have shown a link between air pollution and mortality in adults, less research has been done

on infants. As infants’ lungs are highly susceptible to pollutants, they are particularly vulnerable to

airborne exposure, and more research is required. The available evidence indicates a link between

infant mortality and exposure to HAP and AAP. The links between climate change, air pollution and

infant and child health are also increasingly being studied, with growing concern for the health and

well-being of future generations (2).


Early studies of the effect of AAP on infant mortality consistently found associations of different

strengths. Several studies included in a systematic review in 2005 (3) found strong correlations

between air pollution and infant mortality. The studies were conducted in many geographic areas, on

a range of pollutants, including total suspended particles: SO2, O3, NO2, NOx, PM2.5 and PM10. A

similar review in 2004 found that the results differed by subgroup of infants (4). While inconsistent

findings were noted for PM and both total infant mortality and neonatal mortality, the authors found a

positive association between exposure to PM and post-neonatal mortality. This was especially

pronounced for deaths due to respiratory causes and sudden infant death syndrome. Although the

reason for this trend was not clear, the authors suggested links between exposure to particulate air

pollution and some causes of infant death.

Several studies examined the effects of AAP on infant mortality in more detail. A large study in

Japan was conducted of infant deaths in urban Tokyo between 2002 and 2013 to examine the

association with acute exposure to PM2.5, suspended PM (< 7 µm in diameter) and coarse particles

(PM7–2.5) (5). The mortality rates associated with increases in the concentration of each pollutant of

10 μg/m3 were compared. Infant mortality was categorized by age at death (infant, neonate and post-

neonate) and cause of death. Infant and post-neonatal mortality increased with each 10 μg/m3

increase in PM2.5 and was linked with respiratory diseases. An increase in exposure to coarse particles

was associated with a 21% increase in the risk of post-neonatal mortality; and the risks of post-

neonatal mortality and mortality due to respiratory diseases increased by 10% and 25%, respectively,

with increased concentrations of suspended PM. The risk of infant mortality was increased even

52

when PM concentrations were below the Japanese air quality guideline of a daily average of 35

µg/m3 for PM2.5. These results highlight the importance of evaluating infants separately from other

age groups, as they may be uniquely susceptible to air pollution.

A study with satellite estimates and data from household surveys on the location and timing of almost

1 million births in 30 countries in Africa was conducted to estimate the effect of exposure to ambient

PM2.5 on infant mortality (6). The authors found that infant mortality increased by 9% with a 10

μg/m3 increase in PM2.5 and estimated that exposure to PM2.5 above minimum threshold levels was

responsible for 22% of infant deaths in those 30 countries in 2015.

In an affluent, densely populated area of Flanders, Belgium in close proximity to traffic, industry and

agriculture, researchers investigated acute exposure to PM10 and infant mortality between 1998 and

2006 (7). Daily infant mortality, pollutant level and cause of death were recorded. Neonates aged 2–4

weeks were the most vulnerable to air pollution, with an 11% higher risk of death with every increase

of 10 μg/m3 in PM10. When PM10 levels exceeded 50 μg/m3, these neonates were 1.75 times more

likely to die. This finding is important, because the WHO guidelines recommend levels < 20 μg/m3

and daily averages < 50 μg/m3 on more than 3 days per year (8). Thus, adherence to local pollution

standards may significantly affect infant mortality rates. In this study, infant mortality due to

perinatal circumstances (e.g. maternal conditions, complications of pregnancy and birth, adverse birth

outcomes) and congenital and chromosomal abnormalities also increased with rising daily PM10

levels.

Most studies measured exposure to air pollution immediately before an infant’s death, and relatively

few considered longer exposures, although the extent of exposure to pollutants in the weeks and

months before death may be critical. For example, a correlation has been found between the average

level of PM2.5 during the time between birth and post-neonatal death (9). In a study in a highly

polluted part of California, USA, the average CO, NO2, O3 and PM10 levels experienced by infants 2

weeks and 1, 2 and 6 months before death were measured (10). In infants 28 days to 3 months old,

the risk of death from respiratory causes increased significantly with rising CO levels in the 2 weeks

before death; a moderate increase in risk was seen for infants 4–12 months old with increasing PM10

levels in the 2 weeks before death; and, for infants 7–12 months old, the risk more than doubled when

they had been exposed to high levels of PM10 in the 6 months before death. In another study in the

same region, the risk of sudden infant death syndrome increased by 15–19% when average NO2

levels were elevated during the 2 months before death (11). In a cohort study in the Republic of

Korea of the association between long-term exposure, including during pregnancy and postnatally, to

PM and infant mortality (12), gestational exposure to increasing levels of PM10, total suspended

particulates and PM2.5 increased the risk of infant mortality from all causes and from respiratory

causes in infants with a normal birth weight. The first trimester was the only period during which this

pattern was found independently, indicating an effect of air pollution in early pregnancy on infant

development and mortality.

Policy affects human health and perhaps most significantly that of infants. In a quasi-experiment,

Tanaka (13) studied changes in infant mortality in 175 Chinese prefectures before and after the

introduction of stringent air pollution regulations. In 1998, the power industry, which was heavily

reliant on coal and a major source of emissions (particularly SO2), dramatically reduced its emissions.

This reduction was associated with a 20% decrease in the rate of infant mortality, with a 63%

reduction during the neonatal period, particularly from deaths associated with the nervous and

circulatory systems. The author proposed that the drastic improvement in neonatal survival with

reduced maternal exposure to pollutants benefitted fetal development and increased probability of

survival.


Most of the research on HAP has been on the effects of ambient air pollutants, although infants spend

most of their time indoors. In a study in rural India, the risk of infant mortality was 21% higher in

households with indoor burning of biomass fuels (wood or dung) than in those in which kerosene or

biogas was used (14). In Ecuador, the infant mortality rate increased with the amount of biomass

fuels burnt (15). In these studies, households in which biomass fuels were used were compared with

those in which fuels considered by the authors as “cleaner”, such as biogas, LPG or kerosene, were

used. (As noted above, this categorization contradicts current understanding of the adverse health

impacts of kerosene use.) In another study, infants born to women exposed to polluting cooking fuels

53

(kerosene, charcoal, coal, wood, straw, crop waste and dung) during pregnancy were found to be at

increased risk of neonatal mortality within 0–2 days of birth (16), indicating the danger of exposure

to HAP both in utero and in the domestic environment.


The biological mechanisms through which air pollution increases infant mortality are not clearly

understood. It has been proposed that infants are likely to die when exposed to air pollution because

their immature lungs and immune system leave them unable to cope with the reactive inflammation

that occurs in response to such exposure (17). Pope et al. (18) proposed that PM damages the lung,

resulting in respiratory distress and hypoxaemia. Neonatal rats exposed to PM had reduced cell

proliferation and increased oxidative stress in the lungs (19), but toxicological evidence for humans is

lacking. CO poisoning is a well-known cause of infant death (20).

The association between exposure to PM and adverse birth outcomes, including low birth weight,

preterm delivery and intrauterine growth restriction, is relevant to infant mortality. The possible

mechanisms of these outcomes have been studied in more detail. They may include adverse effects

on the cardiovascular system, such as oxidative stress, inflammation, impaired coagulation and

endothelial function and faulty haemodynamic responses (21). These adverse effects add to the

challenges faced by vulnerable infants as they grow and develop during their first year of life.

Conclusion

A correlation has been found between exposure to HAP and AAP and infant mortality, which

increases with increasing pollution levels. Infants are at particular risk from exposure to PM and toxic

gases. Although inconsistent classification of kerosene in studies of HAP and infant mortality may

affect the interpretation of certain findings, HAP is clearly a risk factor for child mortality.


• Most of the studies addressed acute exposure, and more research is needed to understand the

long-term cumulative effects of air pollution, from gestation to death.

• Few studies to date have been conducted on HAP. Better understanding of the effects of indoor

pollutants on infant mortality will improve understanding of the interaction between exposures

and risk. Studies of the different constituents of air pollution and the biological mechanisms by

which they act will improve understanding of how air pollution affects infant mortality.

• Although sufficient data are available to support preventive action, further research on the

components of air pollution and their mechanisms of action would provide input for additional

preventive policy actions and interventions.

References – infant mortality

1. Levels and trends in child mortality. Report 2017. Estimates developed by the UN Inter-agency Group for

Child Mortality Estimation. New York (NY), Geneva and Washington (DC): United Nations Children’s

Fund, World Health Organization, World Bank and United Nations; 2017.

2. Perera FP. Multiple threats to child health from fossil fuel combustion: impacts of air pollution and climate

change. Environ Health Perspect. 2017;125(2):141.

3. Šrám RJ, Binková B, Dejmek J, Bobak M. Ambient air pollution and pregnancy outcomes: a review of the

literature. Environ Health Perspect. 2005;113(4):375–82.

4. Glinianaia SV, Rankin J, Bell R, Pless-Mulloli T, Howel D. Does particulate air pollution contribute to

infant death? A systematic review. Environ Health Perspect. 2004;112(14):1365–70.

5. Yorifuji T, Kashima S, Doi H. Acute exposure to fine and coarse particulate matter and infant mortality in

Tokyo, Japan (2002–2013). Sci Total Environ. 2016;551:66–72.

6. Heft-Neal S, Burney J, Bendavid E, Burke M. Robust relationship between air quality and infant mortality

in Africa. Nature. 2018;559:254–8.

7. Scheers H, Mwalili SM, Faes C, Fierens F, Nemery B, Nawrot TS. Does air pollution trigger infant

mortality in western Europe? A case-crossover study. Environ Health Perspect. 2011;119(7):1017.

8. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: global


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(http://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf?sequence=

1, accessed August 2018).

9. Carbajal-Arroyo L, Miranda-Soberanis V, Medina-Ramón M, Rojas-Bracho L, Tzintzun G, Solís-Gutiérrez

P, et al. Effect of PM10 and O3 on infant mortality among residents in the Mexico City Metropolitan Area: a

case-crossover analysis, 1997–2005. J Epidemiol Community Health. 2010;65(8):715–21.

10. Woodruff TJ, Parker JD, Schoendorf KC. Fine particulate matter (PM2.5) air pollution and selected causes

of postneonatal infant mortality in California. Environ Health Perspect. 2006;114(5):786.

11. Ritz B, Wilhelm M, Zhao Y. Air pollution and infant death in southern California, 1989–2000. Pediatrics.

2006;118(2):493–502.

12. Son JY, Bell ML, Lee JT. Survival analysis of long-term exposure to different sizes of airborne particulate

matter and risk of infant mortality using a birth cohort in Seoul, Korea. Environ Health Perspect.

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Health Econ. 2015;42:90–103.



growth among newborn infants in south India. Int J Epidemiol. 2009;38(5):1351–63.

15. Rinne ST, Rodas EJ, Rinne ML, Simpson JM, Glickman LT. Use of biomass fuel is associated with infant

mortality and child health in trend analysis. Am J Trop Med Hyg. 2007;76(3):585–91.

16. Patel AB, Meleth S, Pasha O, Goudar SS, Esamai F, Garces AL, et al. Impact of exposure to cooking fuels

on stillbirths, perinatal, very early and late neonatal mortality-a multicenter prospective cohort study in

rural communities in India, Pakistan, Kenya, Zambia and Guatemala. Mat Health Neonatol Perinatol.

2015;1(1):18.

17. Ha EH, Lee JT, Kim H, Hong YC, Lee BE, Park HS, et al. Infant susceptibility of mortality to air pollution

in Seoul, South Korea. Pediatrics. 2003;111(2):284–90.

18. Pope CIII, Arden C, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air

Waste Manage Assoc. 2006;56(6):709–42.

19. Pinkerton KE, Zhou Y, Zhong C, Smith KR, Teague SV, Kennedy IM, et al. Mechanisms of particulate

matter toxicity in neonatal and young adult rat lungs. Res Rep Health Eff Inst. 2008;135:3–41.

20. Goldstein M. Carbon monoxide poisoning. J Emerg Nurs. 2008;34(6):538–42.

21. Kannan S, Misra DP, Dvonch JT, Krishnakumar A. Exposures to airborne particulate matter and adverse

perinatal outcomes: a biologically plausible mechanistic framework for exploring potential. Cienc Saude

Colet. 2007;12(6):1591–602.

5.3 Neurodevelopment

Key findings:

• Exposure to air pollutants can negatively affect neurodevelopment, resulting in lower cognitive

test outcomes (such as global intelligence quotient) and the development of behavioural

disorders such as autism spectrum and attention deficit hyperactivity disorders.

• Research suggests that both prenatal and postnatal exposure to air pollution represent threats to

neurodevelopment.

Overview

Neurodevelopment is a fundamental phase of human growth and development, which begins in the

early prenatal period with the proliferation of radial glia and neurons. While neurodevelopment

continues well into the second decade of life, the first three years of age are especially important.

Various processes occur during this period, including proliferation, migration, differentiation,

synaptogenesis, myelination and apoptosis of neuronal cells (1, 2). If neurodevelopment is

interrupted or impaired by environmental pollutants, the health consequences for the child can be

serious, as this may lead to a number of conditions and symptoms, including cognitive impairment,

attention disorders and autism spectrum disorder, which are difficult to diagnose and treat and may

have lifelong consequences.


Three systematic reviews concluded that there is an association between exposure to AAP, especially

pollutants emitted from vehicles, and impaired neurodevelopment in children (2–4).

Several studies have evaluated the relation between prenatal exposure to air pollution and

neurodevelopment in children and suggested that air pollution can negatively affect their mental and

55

motor development. Lertxundi et al. (5) found that prenatal exposure to PM2.5 and NO2 was

associated with significant decreases in cognitive development and motor development in children at

the age of 15 months. In a study of birth cohorts in the Republic of Korea, prenatal exposure to PM10

and NO2 had significant effects on cognitive development and motor development at 6 months of age

but not at 12 or 24 months (6).

The findings of studies on the effects of prenatal exposure to air pollution on cognitive function and

behaviour have been inconsistent (2). While one meta-analysis of cohort studies in Europe found no

association between cognitive development and exposure to NO2 and PM from traffic-related air

pollution, an association was seen between prenatal exposure to NO2 and deficits in overall

psychomotor function in children aged 1–6 years (7, 8).

Prenatal exposure to air pollutants can have various effects on development throughout childhood. In

Japan, Yorifuji and colleagues (9) reported an association between prenatal exposure to air pollution

and deficits in verbal and fine motor development at the age of 2.5 years. They also found an

association with problems of attention, inhibition and impulsivity at 5.5 years. In the same cohort, the

risks of attention problems and aggressive behaviour were found to have increased by 8 years of age

(10). Other studies indicate that exposure in specific periods during pregnancy is associated with

certain stages of neurodevelopmental deficit, with differences by gender. Chiu et al. (11) reported an

association between exposure to PM2.5 at 31–38 weeks of gestational age and reduced intelligence

quotient among boys and an association between exposure to PM2.5 at 12–20 weeks of gestational age

and decreased general memory index among girls.

Where children live and grow has a powerful effect on their lives. There is increasing evidence that,

postnatally, childhood exposure to traffic-related air pollution is linked to neurodevelopmental

outcomes such as anxiety and depression (12) and impaired cognitive function (13, 14). In a study of

2715 children aged 7–10 years in Barcelona, Spain, Sunyer and colleagues (15) found that children

who attended schools in highly polluted areas had slower growth in cognitive function, measured as

working memory, than those in less polluted areas.

In a prospective study of birth cohort, Suglia et al. (16) used black carbon as an indicator of traffic-

related air pollution and found that increased exposure was associated with lower scores on

intelligence, memory and learning tests in children aged 8–11 years. In a one-year longitudinal study

in Spain, Freire et al. (17) observed that high exposure to traffic-related air pollution was associated

with a modest decrease in cognitive and motor development. A longitudinal study in Spain showed

that students exposed to higher levels of traffic-related NO2, elemental carbon and ultrafine particles

in school classrooms and courtyards had “slower growth in all cognitive measurements” and negative

performance on tests of working memory and attentiveness than those exposed to lower levels. In

another longitudinal study, Chiu et al. (18) found a nonlinear relation between exposure to air

pollution and attention in children aged 7–14. They also found that children in the second and third

quartile of exposure to black carbon made more errors and had a slower reaction time on a

continuous performance task than those in the lowest quartile, although the association was less

strong for those in the highest quartile. Significant associations were found for both boys and girls,

but stronger associations were found for boys.

In the first large study of the effect of air pollution on brain morphology, Guxens et al. (19) analysed

brain imaging scans and cognitive function tests of 783 children in a Dutch birth cohort. Prenatal

exposure to PM2.5 was found to cause structural alterations to the cerebral cortex, which partially

mediates inhibitory control, of children age 6–10 years. Impaired ability to control impulses at this

age may affect educational achievement and increase the risk of mental disorders.


The review of evidence of health effects for the 2014 WHO guidelines for indoor air quality

associated with household fuel combustion (20) identified the association between solid fuel use in

houses and neurodevelopment as an emerging area of research. One study in rural Guatemala found

an association between exposure to CO during pregnancy and reduced neuropsychological

performance in children (21). In another study, memory and building block skills (as indicators of

cognitive development) in children aged 3–9 years in Belize, Kenya, Nepal and American Samoa

were found to be lower in those who were exposed to open-fire cooking (22). The reviewers

concluded that, while research to date suggests a relation between exposure to HAP and impaired

cognitive development, no clear association could be concluded from two studies. Indoor exposure to

CO from cooking with gas or solid fuels may be independently associated with adverse

56

neurodevelopmental outcomes in children (23), but this conclusion is also based on a limited number

of studies.

Autism spectrum disorders

Autism spectrum disorders (ASD) cover a wide range of conditions, which are usually identified by

the age of 5 years. They are characterized by asocial behaviour and difficulties in communication and

language (24). WHO has estimated that one in 160 children currently lives with ASD (24).


Several studies have addressed associations between prenatal and postnatal exposure to traffic-related

air pollution and ASD in children. Becerra et al. (12) reported an increased risk of ASD with

increasing prenatal exposure to NOx, O3, and PM2.5 in Los Angeles, California, citing traffic as the

primary source (12). Three other studies found associations, with an increased risk of ASD and pre-

and postnatal exposure to PM2.5 (13, 14, 25), PM10, NO2 and traffic-related air pollution (14). In a

prospective cohort study, Jung and colleagues (26) identified an increased risk of ASD with rising

levels of CO, NO2, O3 and SO2 in the 1–4 years before diagnosis. In a systematic review of 23

studies, Lam et al. (27) found an increased risk of autism with increased exposure to air pollution but

rated the quality of the evidence as moderate, with a low risk of bias. They concluded that there is

limited evidence of toxicity.

In contrast, two studies in Europe found no association between autistic traits and prenatal exposure

to NO2, PM2.5 or PM10 (28) and no link between pre- and postnatal exposure to NOx and PM2.5 and

ASD (28, 29). Furthermore, a study of birth cohorts in Sweden (30) found no association between

pre- and postnatal exposure to NOx and PM10 and ASD or attention-deficit hyperactivity disorder, a

brain disorder marked by a continuous pattern of inattention and/or hyperactivity–impulsivity that

interferes with functioning or development (31). Lyall et al. (32) suggested that the differences in the

results obtained in Europe and the USA were due to differences in exposure measurements, methods

for assessing ASD and the age at which assessments were done.

Overall, systematic reviews of studies on ASD have shown relatively consistent evidence of an

association between AAP, especially prenatal exposure to PM, and autism (27, 33–36). More

research should be conducted to clarify the effects of individual components of AAP. There is

inconsistent evidence of an association between the critical period of exposure (pre- or postnatal) and

the occurrence of ASD (2, 36, 37).


The review revealed no published studies on HAP from use of polluting fuels and the development of

ASD. ASD were not included in the review of the evidence of health effects for the WHO guidelines

on indoor air quality with respect to household fuel combustion in 2014 (20).


Although neurodevelopment is a complex process, studies are beginning to elucidate the mechanisms

by which air pollution interferes with the normal physiology. A study with magnetic resonance

imaging (MRI) of children aged 7–9 years who had been exposed in utero to PAHs showed a dose–

response relation with reductions in white matter surface (38). The changes were found almost

exclusively in the left hemisphere of the brain and were associated with specific symptoms, including

more severe externalizing behavioural problems, symptoms of attention-deficit hyperactivity disorder

and conduct disorders.

Pujol et al. (39) used MRI to document brain structure, membrane metabolites, functional

connectivity in major neural networks and activation/deactivation dynamics during a sensory task.

Other authors concluded that higher exposure to traffic-related air pollution in childhood slowed

brain maturation (13). Other research suggests that exposure during fetal life to high levels of air

pollution causes structural changes in the cerebral cortex (19).

MRI also revealed significant differences in white matter volume and cognitive deficits between

children living in highly polluted areas and those living in less polluted areas (40). The authors also

saw increased serum inflammatory mediators in these children, suggesting a role for

neuroinflammation, and proposed that structural brain alterations are a potential response to high

57

levels of air pollution. Other work showed an association between long-term exposure to air pollution

(including ultrafine PM and PM2.5) and neuroinflammation, in addition to an altered innate immune

response in children and young adults (41). The authors also noted disruption of the blood–brain

barrier, ultrafine particulate deposition and accumulation of amyloid β-42 and α-synuclein,

suggesting that long-term exposure to air pollution should be considered a risk factor for degenerative

diseases such as Alzheimer and Parkinson diseases.

Conclusions

There is growing evidence of an association between exposure to AAP in the prenatal and postnatal

periods and impaired childhood neurodevelopment. There is strong evidence that exposure to AAP

can negatively affect children’s mental and motor development. There is suggestive evidence of a

link between prenatal exposure to traffic-related air pollution and cognitive and psychomotor

function and behavioural problems, but the findings have been inconsistent. Some studies showed an

association between exposure to HAP and impaired cognitive develoment, but further research is

needed. Outdoor air pollution has been linked to an increased risk of ASD, especially in studies in the

USA in which consistent methods were used.


• Further research should be conducted on the biological pathways of the effects of air pollution

on neurodevelopment. Use of more precise methods for measuring exposure to air pollution, the

composition of PM and the sources, long-term evaluations and identification of critical periods

of exposure would strengthen the evidence of neurodevelopmental effects.

• Long-term neurobehavioural follow-up of children exposed to air pollution in early life is

required to assess the consequences of early exposure later in life in view of the emerging

literature on particulate pollution and dementia in adults.

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5.4 Overweight and obesity

Key findings:

• Some studies suggest an association between exposure to air pollution in utero and postnatal

weight gain or attained BMI for age. Other studies suggest an association between traffic-related

air pollution and insulin resistance in children.

• Air pollution may disrupt the normal development of children, resulting in increased weight-for-

length gain and mean BMI and differences in attained BMI at specific ages. Potential

mechanisms for these effects include regulation of lipid metabolism, fat storage and appetite.

“Overweight” and “obesity” are defined as abnormal or excessive fat accumulation that may impair

health, with weight-for-height greater than two or three standard deviations above the median WHO

child growth standard (1). In practical terms, this means that overweight or obese children are too

heavy for their height.

Childhood obesity is increasing worldwide and is now recognized as a major public health challenge

(2, 3). The prevalence of obesity among young people is high in many countries, but the rate of

obesity is increasing at a faster rate in developing than in developed countries (3, 4). The problem has

reached the proportions of an epidemic: in 2017, 38.3 million children < 5 years and 340 million aged

5–19 years worldwide were overweight or obese (4 ,5). Childhood obesity is likely to continue into

adulthood, with increased risks of cardiovascular or metabolic disorders, including diabetes and heart

disease. Increasingly, obese children are presenting with these diseases early in life. The rapid global

rise in obesity is due to a variety of factors, including overconsumption of energy-dense foods and

less physical activity. The environment in which children are raised can also strongly influence their

risk of becoming overweight or obese (6, 7). The effects of the mother’s environment during

pregnancy must be also be considered. The Commission on Ending Childhood Obesity has

recognized the importance of ensuring that pregnant women are protected from environmental

hazards to reduce the risk of childhood obesity (3). There is growing interest among researchers and

policy-makers in determining the effects on childhood obesity of environmental conditions such as

air pollution (2).


As indicated in section 5.1, exposure to air pollution in utero may affect birth weight through

placental damage, epigenetic changes and maternal inflammatory responses. Longer-term effects on

energy balance, weight-for-length gain and BMI for age in early childhood have also been identified

in some studies. In a study of children in the USA, Rundle et al. (8) reported that prenatal exposure to

PAHs in AAP was associated with increased BMI and obesity in childhood. Pregnant women in this

study wore personal air monitoring devices for 2 days during the third trimester of pregnancy. In

comparison with the group with lowest exposure, children born to mothers most heavily exposed to

PAH had a higher BMI at 5 and 7 years of age and relative risks of obesity of 1.79 and 2.26,

respectively. Adjustment was made for several potentially confounding variables, such as the child’s

sex, ethnicity and birth weight, but did not account for physical activity. Fleisch et al. (9) measured

the weight and length of infants in the Project Viva cohort at birth and at 6 months and determined

the association between prenatal exposure to PM2.5 and black carbon and fetal growth and infant

weight gain. Infants in the highest quartile of exposure to black carbon in the third trimester had less

fetal growth than those in the lowest quartile, and an association was found between exposure to

black carbon or PM2.5 and weight-for-length gain between 0 and 6 months. Account was taken of

60

potentially confounding variables, including weight gain, maternal smoking and abnormal glucose

tolerance.

There is some evidence that air pollution affects different stages of gestation separately. A cohort

study was conducted to determine sensitive periods of exposure and sex-specific effects by modelling

the day and week of exposure to PM2.5 during pregnancy (10). Exposure to PM2.5 during 10–29 weeks

of gestation resulted in increased waist–hip ratios in girls at the age of 4 years. Exposure in weeks 8–

17 and 15–22 of gestation increased the BMI z score and fat mass in boys at the age of 4 years.

Studies in experimental animals also indicate specific differences. A study in rodents found

significant sex-specific differences in weight gain after exposure to diesel exhaust in utero (11).

Maternal health plays an important role in infant and child health and can modify the effects of

environmental exposure. In a study of a cohort of children in the USA, maternal body mass before

pregnancy and exposure to ambient PM2.5 during pregnancy were measured. Children born to

mothers with a high pre-pregnancy BMI and who were exposed to PM2.5 during pregnancy and in the

first 2 years of life had a higher risk of being overweight or obese between 2 and 9 years of age. In

addition, children whose mothers were exposed to PM2.5 at levels above the median were at higher

risk of being overweight or obese, regardless of their mother’s pre-pregnancy BMI (12).

Although there have been few epidemiological studies on the link between exposure to air pollution

and childhood obesity, seven studies were identified in a recent review in which an association was

found between AAP and obesity and metabolic outcomes in children (13). Two prospective birth

cohort studies on air pollution and insulin resistance in children found positive associations between

exposure to traffic-related NO2 and PM10 and insulin resistance in 10-year-old children (14). An

increase in proximity to the nearest major road by 500 metres increased insulin resistance by 7.2%.

Obese or overweight children are thus at increased risk for insulin resistance and other metabolic

complications. Insulin resistance is associated with risks for type 2 diabetes and cardiovascular

disease, which can have profound lifelong effects. In a 4-year longitudinal study, Jerrett et al. (15)

investigated the relation between exposure to traffic-related air pollution and changes in BMI in

children aged 5–11 years in 13 communities in southern California. NOx levels were used as an

indicator of traffic-related air pollution (which can contain black carbon, ultrafine particles and many

PM components). Exposure had a significant effect on BMI growth and BMI level at the age of 10

years. The average annual rate of BMI growth was associated with exposure to NOx in children with

the highest exposure. In another longitudinal study in California (16), children exposed to higher

levels of NOx from traffic-related air pollution had significantly increased BMI growth over 8 years

and a higher attained BMI at 18 years of age as compared with the group with lower exposure.

In a cohort study in Italy (17), no association was found between exposure to NO2, NOx, PM10, PM2.5,

coarse PM or total traffic load within 100 metres of the residence and characteristics including BMI

at the age of 4 years, cholesterol levels and waist circumference at 8 years of age.


There has been no peer-reviewed publication on a link between sources of HAP and childhood

overweight or obesity.


While air pollution’s relation to childhood obesity is a relatively new area of research, some studies

have identified mechanisms by which air pollution may influence childhood obesity and metabolic

dysfunction. Air pollution may influence metabolic development prenatally, as elevated levels of

leptin and adiponectin were found in the umbilical cord blood of infants whose mothers were

exposed to NO2, PM2.5 and NO during pregnancy (18, 19), and these adipokines have been linked to

obesity-related outcomes in childhood (18). In another study, increased levels of leptin and

adiponectin were associated with increased weight gain in infant girls at 6 months of age, suggesting

a pathway for air pollution-mediated risk of obesity in children (19).

The causes of metabolic dysfunction in childhood are complex. In a metabolic profiling study of

overweight or obese young people aged 8–18 living in a highly polluted urban environment, air

pollution was associated with higher insulin resistance and secretion and higher glycaemia (20),

indicating air pollution is a risk factor for type 2 diabetes. In another study, it was reported that

61

children living in Mexico City, where the levels of air pollution (particularly PM2.5 and O3) are high,

had altered appetite-regulating peptides, high blood leptin and endothelin-1 and vitamin D deficiency

(21). Even when the BMI-for-age of the children was below the cut-off for obesity, the analysis

indicated potentially increased risks of insulin resistance, obesity, type 2 diabetes, addiction-like

behaviour and premature cardiovascular disease in adulthood.

Box 9 provides information on stunting and air pollution.

Box 9. Air pollution and stunting

A child who has a low height-for-age is considered to be stunted. In 2012, WHO adopted a global target to

reduce the number of stunted children under the age of 5 by 40% by 2025 (22). In 2017, 150.8 million children

under the age of 5 years were stunted (5). Stunting has both immediate and long-term effects on health and

well-being, as, in addition to poor physical growth, stunted children are more susceptible to infections and have

an increased risk of neurodevelopmental effects, which can affect their school and work performance (23).

Children who are stunted often remain shorter than their peers in adulthood and are at increased risk of

becoming overweight as they grow older. Stunted growth is due mainly to prolonged insufficient caloric intake

or other nutritional deficiencies, but a link with exposure to air pollution has also been proposed in a growing

body of literature on the association between AAP and stunting.

A study of maternal exposure to AAP in Bangladesh showed a strong link between AAP and child stunting

(24). The study was based on outcome data from four waves of the nationally representative Bangladesh

Demographic and Health Survey, conducted between 2004 and 2014. Maternal exposure to AAP (PM2.5) was

estimated from high-resolution satellite data. Over half of all children in the study were exposed to an annual

ambient PM2.5 level > 46 µg/m3, which is over four times the WHO air quality guideline value of 10 µg/m3.

These children were significantly more likely to be stunted. It was concluded that reducing AAP in Bangladesh

could significantly reduce child stunting.

HAP is also strongly linked to stunting. A population-based cohort study of exposure to indoor biomass fuel

and tobacco smoke and the risks of various adverse health outcomes in newborn infants in south India (25)

found that infants exposed to HAP were at a 30% higher risk of being stunted at 6 months of age. The link

between HAP and child stunting is further supported by the results of a systematic review and meta-analysis of

the link between HAP and various adverse health outcomes, including stunting (26). A statistically significant

protective association was found between reduced exposure to HAP and stunting in children under 5 years. The

authors suggested that switching from polluting to clean fuels could substantially reduce the risk of child

stunting and other adverse health outcomes.

Conclusions

Some studies indicate a potential association between exposure to AAP and certain adverse metabolic

outcomes in children. They support the plausibility of the “obesogen” hypothesis, which posits that

exposure to chemical compounds during development can increase susceptibility to gaining weight,

and also the links between childhood obesity, insulin resistance and exposure to air pollution.

Because of the limited number of epidemiological studies of exposure to air pollution and obesity and

insulin resistance, it would be premature to draw conclusions about causality.


• A review of the literature suggests an association between exposure to AAP and childhood

obesity or insulin resistance, but relatively little research has been done on the associations

between childhood overweight and obesity, insulin resistance and air pollution.

• The effect of HAP on childhood overweight and obesity has not been studied, even though

children spend a significant amount of time indoors.

References – overweight and obesity

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2. Maziak W, Ward KD, Stockton MB. Childhood obesity: are we missing the big picture? Obes Rev.

2008;9(1):35–42.

3. WHO Commission on Ending Childhood Obesity (ECHO). Facts and figures on childhood obesity.

Geneva: World Health Organization; 2017 (http://www.who.int/end-childhood-obesity/facts/en/, accessed

August 2018).

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4. NCD Risk Factor Collaboration. Worldwide trends in body-mass index, underweight, overweight, and

obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9

million children, adolescents, and adults. Lancet. 2017;390:2627–42.

5. Joint child malnutrition estimates – 2018 edition. New York City (NY): UNICEF; Geneva: World Health

Organization; Washington DC: The World Bank Group; 2018

(http://www.who.int/nutgrowthdb/estimates2017/en/).

6. Hill JO, Peters JC. Environmental contributions to the obesity epidemic. Science. 1998;280(5368):1371–

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7. Trasande L, Cronk C, Durkin M, Weiss M, Schoeller DA, Gall EA. Environment and obesity in the

National Children’s Study. Environ Health Perspect. 2009;117(2):159–66.

8. Rundle A, Hoepner L, Hassoun A, Oberfield S, Freyer G, Holmes D, et al. Association of childhood

obesity with maternal exposure to ambient air polycyclic aromatic hydrocarbons during pregnancy. Am J

Epidemiol. 2012;175(11):1163–72. 9. Fleisch AF, Rifas-Shiman SL, Koutrakis P, Schwartz JD, Kloog I, Melly S, et al. Prenatal exposure to

traffic pollution: associations with reduced fetal growth and rapid infant weight gain. Epidemiology.

2015;26(1):43–50.

10. Chiu YH, Hsua HHL, Wilson A, Coull BA, Pendof MP, Baccarellig A, et al. Prenatal particulate air

pollution exposure and body composition in urban preschool children: examining sensitive windows and

sex-specific associations. Environ Res. 2017;158:798–805.

11. Bolton JL, Smith SH, Huff NC, Gilmour MI, Foster WM, Auten RL, et al. Prenatal air pollution exposure

induces neuroinflammation and predisposes offspring to weight gain in adulthood in a sex-specific

manner. FASEB J. 2012;26:4743–54.

12. Mao G, Nachman RM, Sun Q, Zhang X, Koehler K, Chen Z, et al. Individual and joint effects of early-life

ambient exposure and maternal prepregnancy obesity on childhood overweight or Obesity. Environ Health

Perspect. 2017;125(6):067005.

13. Vrijheid, M, Casas M, Gascon M, Valvi D, Nieuwenhuijsen M. Environmental pollutants and child health

– a review of recent concerns. Int J Hyg Environ Health. 2016;219(4–5):331–42.

14. Thiering E, Cyrys J, Kratzsch J, Meisinger C, Hoffmann B, Berdel D, et al. Long-term exposure to traffic-

related air pollution and insulin resistance in children: results from the GINIplus and LISAplus birth

cohorts. Diabetologia. 2013;56(8):1696–704.

15. Jerrett M, McConnell R, Wolch J, Chang R, Lam C, Dunton G, et al. Traffic-related air pollution and

obesity formation in children: a longitudinal, multilevel analysis. Environ Health. 2014;13:49.

16. McConnell R, Shen E, Gilliland FD, Jerrett M, Wolch J, Chang CC, et al. A longitudinal cohort study of

body mass index and childhood exposure to secondhand tobacco smoke and air pollution: the Southern

California Children's Health Study. Environ Health Perspect. 2015;123(4):360–6.

17. Fioravanti S, Cesaroni G, Badaloni C, Michelozzi P, Forastiere F, Porta D. Traffic-related air pollution

and childhood obesity in an Italian birth cohort. Environ Res. 2018;160:479–86.

18. Lavigne E, Ashley-Martin J, Dodds L, Arbuckle TE, Hystad P, Johnson M, et al. Air pollution exposure

during pregnancy and fetal markers of metabolic function: the MIREC Study. Am J Epidemiol.

2016;183(9):842–51.

19. Alderete TL, Song AY, Bastain T, Habre R, Toledo-Corral CM, Salam MT, et al. Prenatal traffic-related

air pollution exposures, cord blood adipokines and infant weight. Pediatric Obesity. 2018;13(6):348–56.

20. Toledo-Corral CM, Alderete TL, Habre R, Berhane K, Lurmann FW, Weigensberg MJ, et al. Effects of

air pollution exposure on glucose metabolism in Los Angeles minority children. Pediatr Obes.

2018;13(1):54–62.

21. Calderón-Garcidueñas L, Franco-Lira M, D’angiulli A, Rodríguez-Díaz J, Blaurock-Busch E, Busch Y, et

al. Mexico City normal weight children exposed to high concentrations of ambient PM2.5 show high blood

leptin and endothelin-1, vitamin D deficiency, and food reward hormone dysregulation versus low

pollution controls. Relevance for obesity and Alzheimer disease. Environ Res. 2015;140:579–92.

22. de Onis M, Dewey KG, Borghi E, Onyango AW, Blössner M, Daelmans B, et al. The World Health

Organization’s global target for reducing childhood stunting by 2025: rationale and proposed actions.

Matern Child Nutr. 2013;9(Suppl 2):6–26.

23. Stewart CP, Iannotti L, Dewey KG, Michaelsen KF, Onyango AW. Contextualising complementary

feeding in a broader framework for stunting prevention. Matern Child Nutr. 2013;9(S2):27–45.

24. Goyal N, Canning D. Exposure to ambient fine particulate air pollution in utero as a risk factor for child

stunting in Bangladesh. Int J Environ Res Public Health. 2018;15(1):22.



growth among newborn infants in South India. Int J Epidemiol. 2009;38(5):1351–63.

26. Bruce NG, Dherani MK, Das JK, Balakrishnan K, Adair-Rohani H, Bhutta ZA, et al. (2013). Control of

household air pollution for child survival: estimates for intervention impacts. BMC Public Health.

2013;13(Suppl 3):S8.

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5.5 Respiratory effects

Lung function

Key findings:

• Elevated AAP, particularly traffic-related pollution, impairs lung function and lung function

development in children, even at exposures below United States national ambient air quality

standards.

• Prenatal exposure to air pollution is associated with impairment of lung development and lung

function in childhood.

• There is evidence that lung function development improves in children in urban areas where

ambient air quality has been improved.

Lung function is a measure of how effectively the lungs move air in and out of the body in order to

exchange oxygen with the blood and remove CO2. More simply, lung function indicates how well a

person breathes. The lungs go through dramatic changes during the embryonic and fetal stages and

continue to develop after birth, until late adolescence. Anything that affects the structure of children’s

still-maturing lungs can affect their lung function later in life. Children exposed to air pollution in

utero or in early childhood are thus at risk of compromised lung function for the rest of their lives.


Many studies have shown that exposure to air pollution has negative effects on lung function,

although the effects vary. In the European Study of Cohorts for Air Pollution Effects (ESCAPE),

Gehring et al. (1) found an association between PM2.5 levels at the current address and a small

decrease in lung function in children aged 6–8 years. A prospective cohort study of children in

Taiwan showed that increased exposure to ambient PM2.5 was associated with lower rates of

development in some measures of lung function, including forced vital capacity (FVC) and forced

expiration volume in 1s (FEV1) and with reduced development of FVC (2). Reduced lung function

was also seen in schoolchildren in Hong Kong who had long-term exposure to higher levels of AAP

(3). In four cities in China, Chongqing, Guangzhou, Lanzhou and Wuhan, exposure to ambient PM

was associated with decreased development of lung function in children (4).

The magnitude of the effects on lung function differs by study, perhaps because of spatial differences

in the mass, number and composition of PM. In a study of five European birth cohorts, Eeftens et al.

(5) found a more consistent association between increased PM mass and reduced lung function than

with individual components of PM. They also found small adverse effects associated with exposure

to nickel and sulfur in PM. Overall, these findings suggest that PM mass, rather than specific

components, is more useful for assessing risks from exposure to air pollution to lung development

and function in children.

Traffic-related pollution is a subject of widespread concern. NO2 is commonly used as a reliable

marker of traffic-related air pollution. In a meta-analysis of 13 studies, increased levels of NO2 were

associated with a higher prevalence of children with abnormal lung function (measured in terms of

FEV1) (6). In the Children’s Health Study cohort, independent negative associations were found

between regional and traffic-related air pollution and lung function (7). These authors suggested that

the differences in the strength of the associations reported among studies was due to differences in

the exposure assessment methods used, which included roadway proximity, traffic count and density

measures instead of validated methods.

Some studies reported adverse effects on lung function of pollution at levels below the national

ambient air quality standards of the Environmental Protection Agency in the USA. In a study of the

effect of relatively low exposure to pollution on childhood lung function (8), 614 mother–child pairs

in the Boston area were studied. Long-term exposure to AAP (fine particulate matter and black

carbon) during pregnancy was associated with lower lung function in mid-childhood.

There is evidence that children with asthma are more vulnerable to the effects of air pollution. In

some subgroups of asthmatic children, prenatal and early-life exposure to CO, PM10 and NO2 had

negative effects on pulmonary function (9). A study of children with asthma in two cities in Canada

64

(10) showed that exposure to air pollution was linked to elevated airway oxidative stress and reduced

small airway function. In another study in North America (11), exposure of children with asthma to

air pollution had adverse effects on both lung function and methacholine responsiveness, which is a

measure used to evaluate the degree of bronchial response to external stimulation.

Prenatal exposure to air pollution can impair organogenesis and lung growth, leading to long-term

complications (12). Newborns whose mothers were exposed to high levels of PM10 during pregnancy

had increased minute ventilation and higher respiratory rate and tidal breathing flow (13).

Jedrychowski et al. (14) found that children aged 5 years whose mothers had been exposed to high

levels of PM2.5 during pregnancy had reduced FVC and FEV1. Morales and colleagues (15) reported

lung function deficits in preschool-age Spanish children who had been exposed to traffic-related NO2

and benzene during the second trimester of pregnancy.

In southern California, pollution levels have been decreasing steadily over the past several decades as

a result of air pollution control policies, and there are indications these long-term reductions may

improve the respiratory health of children. A study of lung function measured annually in 2120

children in three cohorts in three separate periods (1994–1998, 1997–2001 and 2007–2011) indicated

an association between reduced exposure to NO2, PM2.5 and PM10 and improved lung function

(measured as FEV1 and FVC) over 4 years (16). The study also showed that the proportion of

children with low FEV1 values decreased as air pollution levels fell. In a study of the same cohort

(17), children who moved from the study area to areas with higher air pollution had lower lung

function growth at follow-up, and children who moved to areas with lower pollution had increased

lung function growth.


Fewer studies have been published on HAP and lung function or lung function development in

children. A randomized controlled trial was performed in rural Guatemala in households with

pregnant women or infants to measure the effects of an intervention to improve indoor air quality on

childhood respiratory health (18). Households in which cooking was traditionally done over an open

fire were randomly selected to receive a chimney stove to improve ventilation of combustion

products from cooking, at the beginning or at the end of the 18-month trial. At the end of the trial,

children in houses that had received the chimney stove had significantly lower longitudinal peak

expiratory flow growth and a large but non-significant decrease in FEV1 growth. Box 10 describes an

intervention in Nigeria to improve the respiratory health of women and children by the introduction

of improved cookstoves.

Box 10. Cleaner stoves, easier breaths (19)

In Nigeria, more than 70% of the population uses solid fuel stoves for cooking. Most household cooking is

done by women, often in poorly ventilated kitchens. This results in high exposure of both women and children

to HAP. A community-based pilot study was conducted in which low-emission stoves were substituted for

traditional biomass stoves in three rural communities: Ajibade, Eruwa and Olorisaoko. Assessments were

conducted before and 1 year after the intervention in households with a mother aged 20–60 years and one or

more children aged 5–17 years. Before substitution of the stove, the PM2.5 levels were found in several cases to

be 60 times greater than the WHO standard, and almost half the mothers and children had diminished

respiratory function. After the intervention, a remarkable decrease was found in the frequency of exposure-

related respiratory symptoms, such as cough, chest tightness, difficulty in breathing and rhinitis, as well as

headaches, fever and dizziness.

In cohorts of children in Chongqing, Guangzhou, Lanzhou and Wuhan, use of coal in houses without

appropriate ventilation was associated with deficits in lung function growth (20). The FVC and FEV1

of exposed children were 27% and 61%, respectively, below the average annual growth levels of the

cohort.

In a small study of lung function in women and children aged 7–15 years in Ecuador, who were

exposed to biomass fuel smoke in the home (21), exposed children had reduced FVC and FEV1.


65

Inhaled particles can be deposited in the bronchioles and the alveoli, where they may affect gas

exchange (22). Small particles, particularly PM2.5, are of interest because their size allows them to

penetrate deep into the lungs, where they cause irritation and induce oxidative stress and

inflammation, damaging lung cells (23). PM is a mixture of physical and chemical components (e.g.

nitrates, sulfates, ammonium, PAHs, allergens, microbial compounds, metals) that can contribute to

lung dysfunction (22, 24).

Prenatal exposure to air pollution can alter lung function and development by various plausible

mechanisms, by causing epigenetic changes in the fetus and negatively affecting the mother’s

respiratory health (25). A prospective birth cohort study of children exposed prenatally to PAHs

indicated that lung function was better when antihistamine medication was used (26), which supports

the theory that the mechanism of fetal PAH-induced alterations in lung function is initiated by the

allergic inflammatory response to pollutants in the lungs.

Conclusion

There is robust evidence that exposure to air pollution damages children’s lung function and impedes

their lung function growth. Even at lower levels of exposure, children – whose lungs are still

maturing and therefore especially vulnerable to pollution – can have lasting deficits in their lung

function. There is also compelling evidence that policies and interventions to improve ambient or

household air quality can lead to improvements in children’s lung function. Compromised lung

function negatively affects quality of life and is associated with long-lasting chronic conditions such

as asthma and chronic obstructive pulmonary disease (27–29).


• Even at exposures to levels below local recommended guidelines, air pollutants significantly

reduced children’s lung function in some studies. More research is required to identify the levels

of pollutants that adversely affect lung function in order to influence policies on air quality.

• More studies are required on the effect of HAP on childhood lung function.

Acute lower respiratory infections, including pneumonia

Key findings:

• Air pollutants such as PM2.5, NO2 and O3 increase the risk of pneumonia and other respiratory

infections in young children.

• Household use of biomass use increases the risk of acute lower respiratory infection, including

pneumonia, in children.

• HAP is the leading cause of acute lower respiratory infection in children under 5 years.

Lower respiratory infections, including pneumonia, bronchitis, bronchiolitis and other acute

respiratory diseases, are the second leading cause of child mortality worldwide. Lower respiratory

infections caused 878 829 deaths in children under 5 years in 2016, accounting for 15.55% of all

child deaths (30, 31). HAP from cooking with solid fuels, AAP and second-hand tobacco smoke were

the causes of 57% of the burden of disease (in DALYs) from lower respiratory infections in children

under 5 years in 2012 (32–34). HAP is the leading risk factor for lower respiratory infections in

children in LMICs, and 13% of lower respiratory infections are attributable to HAP and AAP in

HICs, where the levels of exposure are lower (32).

Pneumonia is an acute respiratory infection caused by viruses, bacteria, fungi or chemicals and is

characterized by inflammation of the air sacs of the lungs (31, 35). While the major environmental

risk factors for pneumonia in children are HAP, AAP and second-hand smoke (35), different

pollutants contribute to respiratory infections in various ways.

66


Short-term exposure to AAP exacerbates acute respiratory infections. Nhung et al. (36) conducted a

meta-analysis of 17 studies on the acute effects of AAP on childhood pneumonia and concluded that

short-term increases in AAP are significantly associated with increased hospital admissions for

pneumonia. Positive associations were found with PM10, PM2.5, SO2, O3 and NO2 in studies

conducted in many counties. Darrow and colleagues (37) investigated the association between short-

term changes in ambient air pollutant concentrations and visits to emergency departments for

respiratory infections. They found that exposure to air pollutants such as PM2.5, NO2 and O3

exacerbates upper respiratory infections and pneumonia in children under 5 years.

Long-term exposure to AAP may also increase the risk for pneumonia in early life. In a meta-analysis

of 10 European birth cohorts, MacIntyre et al. (38) found an association between long-term exposure

to traffic-related air pollution and the incidence of pneumonia. Vehicle traffic is one of the main

sources of exposure to AAP. Rice et al. (39) examined the association between prenatal exposure to

traffic-related air pollution in Boston, USA, and the risk of respiratory infection (including

pneumonia, bronchiolitis and croup) in early life. Reduced distance from roadways and higher traffic

density were correlated with a higher risk of respiratory infection, suggesting that living close to a

major road during pregnancy heightens the risk for respiratory infections in early life.

There is increasing evidence that exposure to PM plays a significant role in acute respiratory

infections. Jedrychowski et al. (40) assessed the effect of prenatal exposure to PM2.5 on the

occurrence of acute bronchitis and pneumonia between birth and 7 years and found that the incidence

of recurrent pulmonary infections was significantly correlated with prenatal PM2.5 exposure in a dose-

dependent manner. Fuertes et al. (41) combined the results for seven birth cohorts to investigate the

effects of various components of PM on the development of pneumonia in early childhood. All the

components (iron, potassium, copper, nickel, sulfur, silicon, vanadium) except zinc from PM10 were

associated with a higher risk of pneumonia in early life.


HAP is not only the largest environmental health risk factor worldwide but is also the leading cause

of acute lower respiratory infection, particularly pneumonia, in children (42). Systematic reviews

show consistent evidence of an association between exposure to HAP and ALRI, especially

pneumonia, in children. A meta-analysis of published observational studies showed that the rate of

ALRI in young children exposed to smoke from household biomass fuel was twice that of children

who were not exposed or who lived in households in which cleaner fuels were used (43). Bruce and

colleagues (44, 45) reviewed 26 studies on non-fatal, severe and fatal ALRI and found an association

with exposure to HAP.

Other systematic reviews have reported higher risks associated with exposure to solid fuel emissions.

These include a comparative risk assessment by Smith et al. (46), a meta-analysis of 10 studies by

Misra et al. (47) and a review of six studies of deaths among children with ALRI in LMICs by

Sonego et al. (48). In a study of acute respiratory infection and ALRI, Po and colleagues (49)

reported a strong association with exposure to solid biofuel in rural children. Although many reviews

noted that the heterogeneity of the studies included was a limitation, the consistency of the findings

suggests an association. Box 11 describes a randomized controlled trial on a clean cookstove

intervention in Guatemala.

A systematic review prepared for the 2014 WHO guidelines for indoor air quality associated with

household fuel combustion (31) concluded that there is substantial evidence that solid-fuel HAP

increases the risk of ALRI and that the risk of severe and fatal ALRI may be more than doubled. The

authors concluded, however, there is relatively limited evidence on the mechanisms by which HAP

causes pneumonia in children.

Box 11. Breathing lessons: a randomized control trial in Guatemala yields insights on clean cooking and

children’s health

The RESPIRE study, the first randomized controlled trial on the health effects of cooking interventions, was

conducted in the rural highlands of San Marcos in Guatemala between October 2002 and December 2004. The

region’s inhabitants typically used open wood fires for cooking and heating, resulting in long-term exposure to

HAP, particularly for women and children. The aim of the study was to determine whether reducing smoke

67

from a plancha chimney stove would reduce the risk of pneumonia in children < 18 months of age. After

visiting more than 5000 households, those in which there was a pregnant woman or children under 4 months of

age were selected for the trial. The intervention consisted of providing some households with a closed chimney

plancha. Levels of CO in the home were monitored, and the children were followed for 18 months at periodic

visits by field workers and underwent medical examinations, which included monitoring of hypoxaemia and

chest X-rays if pneumonia was diagnosed. The researchers found a 33% reduction in diagnosed cases of severe

pneumonia in children living in households with the improved stoves, suggesting that exposure to pollution

from household fuel combustion plays a role in the pathogenesis of pneumonia and that substitution of clean

fuels and devices for cooking and heating may reduce the incidence of pneumonia in children (50).

Several studies in the same cohort were performed in parallel. One found that exposure in utero to smoke from

open wood stoves increased the risk of adverse neurodevelopment outcomes over that of children in households

with a stove that had an enclosed combustion chamber, such as a plancha (51). Another found a lower

incidence of children born with low birth weight to mothers living in households with a plancha (52).


Pollutants contribute to respiratory infections by several mechanisms. Inhaled PM can damage the

normal defence mechanisms of the respiratory tract by causing inflammation and oxidative stress,

and breathing NO2, which is a free radical, can also damage and inflame the respiratory tract. There is

some evidence that combustion-derived PM interferes with alveolar macrophages, which have an

essential role in the response of the immune system to viruses and bacterial infections, therefore

increasing the susceptibility of individuals to infections. Laboratory analysis of human macrophages

exposed to HAP showed impaired ability to phagocytose Streptococcus pneumoniae and

Mycobacterium tuberculosis and a lower oxidative burst capacity, suggesting reduced host defence

against infection (53). Box 12 discusses the association between air pollution and TB.

A laboratory study showed that exposure to black carbon alters the biofilm structure, composition

and function of Staphylococcus aureus and Streptococcus pneumoniae and their tolerance to

proteolytic degradation and response to antibiotics (54). Furthermore, black carbon caused S.

pneumoniae to spread from the nasopharynx to the lungs in an animal model. These results have

important implications for the pathways by which air pollution may cause lung infections in children.

Box 12. Air pollution and tuberculosis

TB is an infectious disease caused by Mycobacterium tuberculosis. It affects mainly the lungs but can also

spread to other body systems. It is transmitted from person to person through the air. Disease progression may

be influenced by environmental factors, and air pollution has been identified as a potential risk factor for active

TB in adults in several studies. There is a growing scientific evidence that both AAP and HAP are associated

with TB in children, but no systematic reviews specifically on studies in children have been published, and the

risk of childhood TB associated with exposure to AAP has not yet been evaluated. An ecological study

conducted in North Carolina, USA, showed a significant association between pulmonary TB and long-term

exposure to PM. Children and adolescents aged 0–24 years were included in the study, but their risk was not

independently assessed (55). Another study in adults in California, USA, also found a relation between

residential exposure to PM2.5 and the presence of smear-positive acid-fast bacilli (56).

The association between exposure to HAP from solid fuel combustion and TB has been evaluated in a few

studies. Exposure to biomass fuel combustion exhaust was found to prevent macrophages in the lung from

functioning correctly (57). As macrophages have a key role in the immune response to infection, these changes

may increase the vulnerability of individuals to TB and other respiratory infections (58). A meta-analysis of

studies in children and adults performed in 2014 (59) showed a relation between HAP from solid fuel

combustion and the risk of TB. Two of the studies included in the review were specifically of children, and

both found positive but nonsignificant associations. A case–control study in India showed a correlation between

exposure to HAP from solid fuel combustion and the risk of contracting TB in children < 5 years (60). The

evidence suggests a positive association between exposure to air pollution and TB infection in children,

although further research is needed.

Conclusions

Many studies offer consistent, compelling evidence that exposure to AAP or HAP is a major risk

factor for ALRI in children. It is clear that exposure to air pollution increases the incidence of ALRI,

68

including pneumonia. While a range of pollutants has been found to exacerbate respiratory infections,

there is growing evidence that PM has an especially strong effect.


• There is a lack of studies on associations between exposure to specific chemical components of

ambient particulate matter and ALRI such as pneumonia in young children.

• Research on the mechanisms through which air pollution induces lung infection will aid in

identification of treatments and preventive measures to protect children from serious, life-

threatening illness.

Asthma

Key findings:

• There is evidence of a causal relationship between exposure to AAP and the development and

exacerbation of childhood asthma.

• There is suggestive evidence of a causal effect of exposure to HAP and the development and

exacerbation of asthma in children.

Asthma affects an estimated 250 million people worldwide and is a common chronic illness in

children (61). Both AAP and HAP have long been suspected of contributing to childhood asthma,

and a growing body of research suggests that exposure to air pollution both causes and exacerbates

the condition. As children have narrower airways and higher breathing rates than adults, they are

particularly vulnerable to airborne pollution. Furthermore, children tend to spend much time doing

physical activity outdoors and breathe through their mouths more frequently than adults, allowing

more unfiltered air pollutants to affect their still-developing lungs.

Asthma development


In a meta-analysis of 19 studies conducted between 1996 and 2012, Gasana et al. (62) observed a

positive association between exposure to NO2 and the incidence of asthma and between exposure to

PM and higher incidence of wheeze in children. In addition, they found a higher prevalence of

wheeze in children exposed to SO2 and a higher prevalence of asthma associated with exposure to

NO2, nitrous oxide and CO. A systematic review and meta-analysis by Khreis and colleagues (63) of

41 studies indicated significant associations between increased exposure to PM2.5, PM10, NO2 and

black carbon and the risk of asthma.

The longitudinal association between early childhood exposure to AAP and future asthma incidence

has been evaluated in several cohort studies. In a meta-analysis of published birth cohort studies,

Bowatte et al. (64) reported significant associations between long-term exposure to black carbon and

PM2.5 and the risk of asthma in childhood. They also reported an association between exposure to

traffic-related air pollution in early childhood and a higher risk of developing asthma up to 12 years

of age. Gehring et al. (65) evaluated the longitudinal association between prenatal exposure to air

pollution and development of asthma throughout childhood and adolescence in four prospective birth

cohort studies in Europe. They found that increased exposure to NO2 and PM2.5 at the birth address

was associated with an increased risk for asthma throughout childhood and adolescence. Sbihi et al.

(66) analysed a population-based birth cohort of 65,254 children in Vancouver, Canada, and found

positive association between perinatal exposure to air pollution and asthma incidence during pre-

school years.

Two systematic reviews reached similar conclusions on the role of long-term exposure to AAP in

asthma development, further strengthening evidence of an association. A systematic review of 18

studies (67) found evidence of a significant link between prenatal exposure to NO2, SO2 and PM10

and the development of asthma. The authors found insufficient evidence that exposure to black

carbon, CO or O3 during pregnancy was associated with asthma in childhood. Andersen and

colleagues (68) reviewed 17 cohort studies and found that 12 showed positive associations between

exposure to air pollution and the incidence of asthma.

69


Several studies found positive associations between indoor cooking with polluting fuels and asthma

development in children. In a meta-analysis of 41 studies published before 2013, Lin et al. (69) found

a positive association between gas cooking, exposure to NO2 and childhood asthma or wheeze. In a

study of over 512 000 children in primary and secondary schools in 47 countries, Wong et al. (70)

found a link between cooking on an open fire and the risk of reported asthma in both boys and girls.

Studies in India (71) and Nepal (72) also found statistically significant increases in the risk of asthma

with indoor use of biomass fuel stoves, especially in the absence of appropriate ventilation. In

contrast, a study in Malaysia found no association between exposure to household wood stoves and a

first hospitalization for asthma of children aged 1 month to 5 years (73). The authors also found no

association with other factors, such as use of kerosene stoves, aerosol mosquito repellent or

crowding.

Asthma exacerbation


Numerous studies have found that exposure to PM1, PM2.5 and PM10 exacerbates asthma, aggravating

the symptoms of wheeze and shortness of breath (74, 75). A meta-analysis of 26 studies conducted in

Australia, Canada, China, Denmark, Finland, Turkey and the USA (76) found a 4.8% increase in the

risk of asthma-associated emergency department visits and admissions among children exposed to

short-term increases in PM2.5 of 10 μg/m3. The effect was greater in studies conducted in Europe and

North America than in Asia. Other meta-analyses have found similar results. A review revealed a

3.6% increase in risk of emergency visits and admissions for asthma of children per 10 μg/m3

increase in ambient PM2.5 (77). Zheng and colleagues (78) also reported associations between

exposure to PM2.5, O3, CO, NO2, SO2 and PM10, and hospital admissions in 50 studies of children.

Meta-analyses by Weinmayr and colleagues (79) showed associations with PM10, and Orellano and

colleagues (80) found associations with NO2, SO2 and PM2.5.

Zhang et al. (81) conducted a meta-analysis of 26 studies conducted in the East Asian region and

found associations between exposure to ambient NO2, SO2, CO, and PM10 and asthma-related use of

general and emergency hospitals. The association between exposure to air pollution and asthma

morbidity was generally stronger in children < 15 years than in other age groups. The results of

studies in southern California, USA, where the levels of air pollution have been decreasing for

several decades as a result of air pollution controls, show that decreases in AAP levels were

associated with statistically significant decreases in bronchitic symptoms among children with

asthma (82). These meta-analyses together provide compelling evidence that exposure to a range of

ambient air pollutants places children at higher risk of asthma-related hospitalization.

Box 13. Case study: Identifying AAP as a trigger for asthma exacerbation3

Megan, a 9-year-old living in Alberta, Canada, was diagnosed with asthma 5 years ago. Since then, her

symptoms have been well controlled by a common treatment: inhaled steroids combined with a long-acting beta

agonist, and a short-acting beta agonist as needed. In the past 2 weeks, however, she had had several episodes

of breathlessness while training outside with the school track team, which she joined recently. On days spent at

home, her symptoms diminished. She had no allergies, and spring had not yet arrived in the region. Her home

was in a quiet suburban neighbourhood, with no highways or industrial areas nearby, and close to a park. Her

parents didn’t smoke or keep pets, and the home did not have a solid-fuel stove.

Her school, however, was on a busy street. Drivers often left their engines running while picking up or

dropping off passengers. The school playground was regularly sprayed with herbicides. Her paediatrician

concluded that the exacerbation of her asthma was probably related to her exposure to herbicides in the school

playground and to AAP from motor vehicles. The paediatrician recommended that the family check the Air

Quality Health Index, Canada’s local air quality monitoring system, daily basis before Megan participated in

outdoor activities. The doctor also advised that Megan be given an extra dose of her regular asthma medication

3 Presented at Workshop 6: Protect the children! What you can do to prevent environmental hazards from harming children.

28th International Congress of Pediatrics, 17–22 August 2016, Vancouver, Canada.

70

before she engaged in sports. The school principal was notified and decided to implement a “no-idling” policy

for vehicles. Parents advocated to stop herbicide being sprayed in the playground and discussed with teachers

the possibility of training inside on days when AAP levels were higher than recommended for outdoor

activities.

Asthma is a multifaceted condition. In Megan’s case, the exacerbation appeared to be related to poor air quality

due to heavy traffic, use of herbicides and idling of vehicles near the training area. After a few weeks of

alternating indoor–outdoor training when pollution levels were high, cessation of herbicide use and the no-

idling policy, Megan’s episodes of breathlessness diminished, and her extra treatment for asthma was no longer

necessary.


Although the relation between exposure to AAP and exacerbation of childhood asthma has been well

documented, there is less evidence on exposure to HAP from incomplete combustion of polluting

fuels. The review for the WHO guidelines for indoor air quality associated with household fuel

combustion (31) concluded that there is suggestive evidence for a causal effect of exposure to HAP

and exacerbation of asthma in children. A cross-sectional study in rural Nigeria of 1690 school-age

children (19) found that living in a household in which biomass fuel was used for cooking increased

the risk of severe asthma symptoms. Schei and colleagues (83) concluded that use of open fires

increased the risk of asthma symptoms in children aged 4–6 years living in an indigenous Maya

community in Guatemala.

Box 14 describes a community activity for managing asthma in children.


Many pathways have been studied through which air pollution may contribute to childhood asthma.

Inflammation and oxidative stress are known harmful effects of air pollution. In a study of children

with asthma living in a highly polluted environment, elevated levels of SO2, NO2 and benzene were

associated with increased bronchial inflammation and biological markers of oxidative damage and

asthma symptoms (84). Research also indicates that epigenetic modification of DNA plays a role in

the association between childhood asthma and air pollution (85). The patterns of DNA methylation

that contributes to lung damage in children with asthma in response to air pollution have been

identified (86), and another study indicated that DNA methylation associated with prenatal exposure

to PAHs may contribute to the development of childhood asthma by altering gene expression early in

life (84), but more research is needed.

Box 14. “You can control asthma now”.

An award-winning initiative demonstrates effective community engagement in managing asthma in children, at

the Children’s Hospital of Richmond, at Virginia Commonwealth University in the USA (84). The hospital

formulated a promising programme called “You can control asthma now” in response to the disproportionately

high burden of disease attributed to asthma, compounded by poverty, in its region.

When a child is first diagnosed with asthma, the family is directed to the unit by their general practitioner or the

emergency room. The unit is staffed by a pulmonologist, a nurse and social workers, who use a

multidisciplinary approach for clinical assessments, education and providing support and resources to address

barriers to treatment. Practitioners follow up families over the long term by text or phone communications. The

home environment is assessed by the City of Richmond Health District, and families can be referred to a

medical legal partnership programme to help them resolve any environmental problems in the home that might

affect their child’s asthma. The programme offers extensive practical information to families on the effect of

environmental exposures on childhood asthma and how to make changes.

Since 2015, the programme has assisted more than 344 patients with family-focused management techniques.

The programme has also made the region’s health care system more cost-effective, saving US$ 691 per patient

by fewer hospitalizations and emergency room visits, which adds up to a total cost reduction of US$ 163 958

since the programme began. The initiative was awarded the “Asthma award” of the Environmental Protection

Agency in 2017 in recognition of a successful asthma management intervention that is integrated into health

care services. The programme shows that, through collaboration and engagement with the community and the

development of specific resources, families can reduce environmental exposures to protect the health of

children.

71

Conclusion

The relation between air pollution and childhood asthma is clear. Many studies provide consistent,

robust evidence of an association between exposure to air pollution and the risk of developing asthma

in childhood. There is also ample evidence that breathing pollutants exacerbates asthma in children.

Although the mechanisms are not as well understood, long-term exposure to PM and other pollutants

can increase the probability that a child will develop asthma, with serious long-term implications for

health and quality of life. While there are fewer studies on HAP, they provide sufficient evidence to

support proactive approaches to limit children’s exposure to both AAP and HAP to protect them from

developing and exacerbating asthma.


• Various epidemiological studies support the conclusion that AAP and traffic-related air pollution

are related to exacerbation and development of asthma in children. The vulnerable period of

exposure for childhood asthma remains to be defined, and more long-term birth cohort studies

with regular, repeated follow-up are needed.

• Although there is suggestive evidence of an effect of exposure to HAP on asthma development

and exacerbation, additional studies with consistent methods and exposure assessment and

intervention studies are necessary to confirm a causal relation.

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5.6 Otitis media

Key findings:

• An increasing number of epidemiological studies indicate an association between exposure to

AAP and the occurrence of otitis media in children.

• HAP from combustion may increase the risk of otitis media.

Otitis media, inflammation of the middle ear, is a common childhood infection (1). Viral and

bacterial ear infections are the primary causes of otitis media, which often occurs with upper

respiratory tract infections (2). Environmental exposures also play a role. Exposure to second-hand

tobacco smoke is a known risk factor (3, 4), and evidence suggests that exposure to AAP and HAP

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may have a similar effect on the development of otitis media. A child with recurrent otitis media can

have long-term consequences, such as hearing loss, and potential difficulties in learning and

communication (5, 6).


AAP has been strongly linked to otitis media in children. A review (2) of five cross-sectional, two

time-series and three cohort studies found a higher prevalence of otitis media in children living in

areas with high levels of AAP (7–10). Traffic-related air pollution was associated with risk for otitis

media (2) and with higher risks for ear, nose and throat infections (11). In a study of over 7000

children in Germany (12), the prevalence of otitis media decreased over a 7-year period in areas in

which air quality improved. Strong conclusions could not be drawn from the review because of the

limited number of studies.

A systematic review of 24 studies (1) found limited but increasing evidence of a link between

exposure to AAP and otitis media in children. All the studies found evidence of a positive association

with AAP, but the results were inconsistent for most pollutants, except NO2.

Other studies have reported a higher incidence of otitis media among children exposed to air

pollutants, especially NO2 and PM2.5. Brauer et al. (13) observed an association between exposure to

traffic-related air pollutants (NO2, PM2.5 and elemental carbon) and the incidence of otitis media in

the first 2 years of life in two large birth cohorts in Germany and the Netherlands. Zemak and

colleagues (3) analysed emergency department visits by children aged 1–3 years for otitis media over

10 years in Canada and found an association with exposure to CO and NO2. In another study in

Canada (14), 42,413 children born in British Columbia were followed until 2 years of age. The

authors found that the average levels of exposure to pollutants (NO, CO, PM2.5 and wood smoke) in

their residence 2 months before hospital visits were associated with the occurrence of otitis media. In

a study in Spain, Aguilera et al. (15) reported a significant association between otitis media in early

childhood and exposure to NO2 and benzene during pregnancy. A meta-analysis of 10 European birth

cohort studies of the effects of traffic-related air pollution (NO2, NOx, PM2.5, PM10 and PM2.5-10) on

otitis media (16) showed an association with the annual average ambient NO2 concentration during

the first year of life.

Time-series and case-crossover studies have also reported positive associations between exposure to

AAP and visits to an emergency department for otitis media. In an analysis of 4815 such visits by

children aged < 3 years in Ontario, Canada (17), the number of visits increased in the days after an

increase in the ambient levels of O3 and PM. A study of 422 268 emergency department visits for

otitis media between 2002 and 2008 in Georgia, USA, found associations with exposure to CO, NO2,

O3, PM10, PM2.5, element carbon, organic carbon ammonium and SO42- (18).

The evidence indicates a consistent association between exposure to air pollution and otitis media.


An association between parental tobacco smoking and otitis media in young children has been well

documented (4, 19–21); however, few studies have been conducted on the associations with other

sources of HAP, particularly in low-income countries (20).

A systematic review of risk factors for chronic and recurrent otitis media (20) identified only one

study on HAP (22), in which indoor cooking was associated with chronic suppurative otitis media.

Older studies in high-income countries gave inconsistent results. In a case–control study of 125 otitis

media patients and 237 controls in a private paediatric practice in New York, USA, between October

1986 and May 1987, exposure to a wood-burning stove was associated with otitis media (23). A

study of more than 900 infants in two states in the USA (24) found no significant association between

otitis media and secondary heating sources (fireplace, wood stove, kerosene heater and air

conditioning).

Among the few studies from LMICs is a case–control study in Maputo, Mozambique (25), which

showed an association between use of wood and charcoal as household fuels and the occurrence of

otitis media. A study of 189 children living in urban areas in two Nigerian states (26) indicated that

indoor cooking was significantly associated with the occurrence of chronic suppurative otitis media;

however, the type of fuel used was not specified.

76

Multiple sources of exposure

It is important to consider the timing of exposure to household and ambient air pollutants. Deng et al.

(26) conducted a retrospective cohort study of 1617 children aged 3–4 years in Changsha, China. The

lifetime prevalence of otitis media in preschool children was associated with prenatal exposure to an

industrial air pollutant (SO2) and postnatal exposure to indoor renovations. Both AAP from industrial

activities and HAP from renovations were associated with development of early childhood ear

infection.


The biological pathways through which air pollution contributes to otitis media in children are not

clear. Epithelial cells of the middle ear had significantly altered gene expression in response to

exposure to PM (27), and the authors noted that some of the genes affected are involved in cellular

processes, including generation of reactive oxygen species, apoptosis, cell proliferation, cell

differentiation and inflammatory response. These may therefore be triggered by exposure to PM.

In another study, increased mucin gene expression (which can contribute to chronic infection),

decreased cell viability and an increased inflammatory response were observed as a result of

exposure to diesel exhaust particles (28). These findings were supported by the results of a study in

experimental animals. Further research will help to confirm whether these processes play a role in the

development of otitis media in children.

Conclusions

There is consistent evidence of an association between exposure to AAP and otitis media in children.

The findings on the effects of individual pollutants are not consistent, and few studies of HAP are

available.


• Prospective observational epidemiological studies on the association between AAP exposure and

otitis media occurrence should be undertaken.

• Evidence from studies of HAP is limited. As infants and children spend much of their time in the

home, more studies should be conducted, with detailed measurements of HAP.

• Studies on the mechanisms by which air pollution contributes to the development of otitis media

in children should be undertaken.

References – otitis media

1. Bowatte G, Tham R, Perret JL, Bloom MS, Dong G, Waidyatillake N, et al. Air pollution and otitis media

in children: a systematic review of literature. Int J Environ Res Public Health. 2018;15:257.

2. Heinrich J, Raghuyamshi VS. Air pollution and otitis media: a review of evidence from epidemiologic

studies. Curr Allergy Asthma Rep. 2004;4(4):302–9.

3. Zemek R, Szyszkowicz M, Rowe BH. Air pollution and emergency department visits for otitis media: a

case-crossover study in Edmonton, Canada. Environ Health Perspect. 2010;118(11):1631.

4. The health consequences of involuntary smoking. Washington DC: Department of Health and Human

Services; 1986.

5. Qureish A, Lee Y, Belfield K, Birchall JP, Daniel M. Update on otitis media – prevention and treatment.

Infect Drug Resist. 2014;7:15–24.

6. Williams CJ, Jacobs AM. The impact of otitis media on cognitive and educational outcomes. Med J Aust.

2009;191(9 Suppl):S69–72.

7. Dostal M, Hertz-Picciotto I, James R, Keller J, Dejmek J, Selevan S, et al. Childhood morbidity and air

pollution in the Teplice program. Cas Lek Ces. 2001;140(21):658–61.

8. Cáceres Udina M, Alvarez Martínez JA, Argente del Castillo J, Chumilla Valderas MA, Fernández

Alvarez E, Garrido Romera A, et al. Incidencia, contaminación ambiental y factores de riesgo de otitis

77

media aguda en el primer año de vida: estudio prospectivo [Incidence, air pollution and risk factors of

acute otitis media in the first year of life: a prospective study]. An Pediatr (Barc). 2004;60(2):113–205.

9. Holtby I, Elliott K, Kumar U. Is there a relationship between proximity to industry and the occurrence of

otitis media with effusion in school entrant children? Public Health. 1997;111(2):89–91.

10. Ribeiro H, Cardoso MRA. Air pollution and children’s health in São Paulo (1986–1998). Soc Sci Med.

2003;57(11):2013–22.

11. Brauer M, Hoek G, Van Vliet P, Meliefste K, Fischer PH, Wijga A, et al., Air pollution from traffic and

the development of respiratory infections and asthmatic and allergic symptoms in children. Am J Resp

Crit Care Med. 2002;166(8):1092–8.

12. Heinrich J, Hoelscher B, Frye C, Meyer I, Pitz M, Cyrys J, et al. Improved air quality in reunified

Germany and decreases in respiratory symptoms. Epidemiology. 2002;13(4):394–401.

13. Brauer M, Gehring U, Brunekreef B, de Jongste J, Gerritsen J, Rovers M, et al. Traffic-related air

pollution and otitis media. Environ Health Perspect. 2006;114(9):1414.

14. MacIntyre EA, Karr CJ, Koehoorn M, Demers PA, Tamburic L, Lencar C, et al., Residential air pollution

and otitis media during the first two years of life. Epidemiology. 2011;22(1):81–9.

15. Aguilera I, Pedersen M, Garcia-Esteban R, Ballester F, Basterrechea M, Esplugues A, et al. Early-life

exposure to outdoor air pollution and respiratory health, ear infections, and eczema in infants from the

INMA study. Environ Health Perspect. 2013;121(3):387.

16. MacIntyre EA, Gehring U, Mölter A, Fuertes E, Klümper C, Krämer U, et al. Air pollution and respiratory

infections during early childhood: an analysis of 10 European birth cohorts within the ESCAPE Project.

Environ Health Perspect. 2014;122(1):107.

17. Kousha T, Castner J. The air quality health index and emergency department visits for otitis media. J Nurs

Scholarsh. 2016;48(2):163–71.

18. Xiao Q, Liu Y, Mulholland JA, Russell AG, Darrow LA, Tolbert PE, et al. Pediatric emergency

department visits and ambient air pollution in the US state of Georgia: a case-crossover study. Environ

Health. 2016;15(1):115.

19. Jones LL, Hassanien A, Cook DG, Britton J, Leonardi-Bee J. Parental smoking and the risk of middle ear

disease in children: a systematic review and meta-analysis. Arch Pediatr Adolesc Med. 2012;166:18–27.

20. Zhang Y, Xu M, Zhang J, Zeng L, Wang Y, Zheng QY. Risk factors for chronic recurrent otitis media – a

meta-analysis. PLoS One. 2014;9(1):e86397.

21. Nandasena S, Wickremasinghe AR, Sathiakumar N. Indoor air pollution and respiratory health of children

in the developing world. World J Clin Pediatr. 2013;2(2):6–15.

22. Lasisi AO, Olaniyan FA, Muibi SA, Azeez IA, Abdulwasiu KG, Lasisi TJ, et al. Clinical and

demographic risk factors associated with chronic suppurative otitis media. Int J Pediatr Otorhinolaryngol.

2007;71(10):1549–54.

23. Daigler GE, Markello SJ, Cummings KM. The effect of indoor air pollutants on otitis media and asthma in

children. Laryngoscope. 1991;101(3):293–6.

24. Pettigrew MM, Gent JF, Triche EW, Belanger KD, Bracken MB. Leaderer BP, et al., Infant otitis media

and the use of secondary heating sources. Epidemiology. 2004;15(1):13–20.

25. da Costa JL, Navarro A,Branco NevesJ, Martin M. Household wood and charcoal smoke increases risk of

otitis media in childhood in Maputo. Int J Epidemiol. 2004;33(3):573–8.

26. Deng Q, Lu C, Jiang W, Zhao J, Deng L, Xiang Y. Association of outdoor air pollution and indoor

renovation with early childhood ear infection in China. Chemosphere. 2017;169:288–96.

27. Song JJ, Kwon JY, Park MK, Seo YR. Microarray analysis of gene expression alteration in human middle

ear epithelial cells induced by micro particle. Int J Pediatr Otorhinolaryngol. 2013;77(10):1760–4.

28. Park MK, Chae SW, Kim HB, Cho JG, Song JJ. Middle ear inflammation of rat induced by urban

particles. Int J Pediatr Otorhinolaryngol. 2014;78:2193–7.

5.7 Cancer

Key findings:

• There is substantial evidence that exposure to traffic-related air pollution is associated with

childhood leukaemia.

• Several studies have found associations between prenatal exposure to AAP and higher risks of

retinoblastoma and leukaemia in children.

• Relatively few studies have been conducted on HAP and cancer risk in children. Nevertheless,

HAP is strongly associated with several types of cancer in adults and commonly contains a

variety of classified carcinogens.

The incidence of cancer in children is increasing, as shown by data from 68 countries and over 100

population-based registries published by the International Agency for Research on Cancer (IARC)

(1). In the period 1990–2017, an average of 215 000 cases of cancers per year were diagnosed in

78

children under 15 years of age, and 85 000 new cases were diagnosed among those aged 15–19 years

(1). In view of the lack of cancer registries in several low-income countries, however, these statistics

may be underestimates of the actual incidence (2).

The most prevalent types of cancer are different in children and adults (3). Leukaemia and lymphoma

are the most common in children, accounting for almost half of all childhood cancers, followed by

central nervous system tumours and tumours originating in embryonic tissues, such as

neuroblastoma, retinoblastoma and nephroblastoma. Children also develop carcinomas, but the

incidence is low (2).


Children are exposed to a wide range of cancer-causing pollutants in ambient air. Diesel exhaust,

AAP and particulate matter have been classified by working groups convened by IARC as Group 1

carcinogens. Nitroarenes, which are derived from diesel engine emissions, have been classified as

Group 2 carcinogens, and gasoline exhaust has been classified as a Group 2B carcinogen (possibly

carcinogenic to humans) (4). Traffic exhaust also contains harmful contaminants, such as CO, PAHs,

benzene, NOx and PM (5). Benzene has been classified in Group 1 (carcinogenic to humans) (5, 6).

Leukaemia is the most frequent childhood cancer (2). Although the etiology of at least 90% of cases

of leukaemia remains unknown in (7), many studies have shown that exposure to traffic-related

pollution (including diesel and gasoline exhaust) is associated with childhood leukaemia (4, 8, 9). A

meta-analysis indicated that the development of leukaemia in early childhood is associated with

exposure to traffic during the postnatal period, with a risk increased by 1.5 times (9). In addition,

exposure to PM10 was independently associated with the risk for leukaemia. In a meta-analysis on the

role of benzene in the pathogenesis of childhood leukaemia, traffic-related exposure to benzene

increased the risk for acute myeloid leukaemia by a factor of 2.07 and the risk for acute

lymphoblastic leukaemia by 1.49 (10).

The relation between proximity to highways, urban AAP and childhood cancer has also been

assessed. A study in a nationwide cohort in Switzerland found that the risk for leukaemia of children

who lived < 100 M from a highway was 1.43 times greater than that of children who lived > 500 M

away, especially for those < 5 years of age (11). Box 14 illustrates differences in childhood cancer

risk according to residence.

Box 14. Location matters: variations in air pollution and childhood cancer risk in a city in Turkey

A study in Turkey was conducted to assess the relation between exposure to benzene, toluene, ethyl benzene,

xylenes, NO2 and O3 and childhood cancer risk in two areas of the city of Eskisehir (12). Students at two

schools participated: one in an urban area known to have high levels of air pollutants and one in a suburban

location with lower levels of pollution. Benzene, toluene, ethyl benzene and xylenes are volatile organic

compounds considered to be hazardous air pollutants; benzene is a Group 1 human carcinogen (5).

Personal air sampling and indoor (school and home) and outdoor air sampling was conducted over a 24-h

period. Children who lived in smoking and in non-smoking homes were identified. An activity diary was given

to each child, with a questionnaire on socioeconomic status, family activities and house characteristics (e.g.

floor type, renovations). Potential sources of pollutants at residences and schools were identified from a

checklist. Personal, exposure to indoor and outdoor concentrations of all air pollutants except O3 was higher for

children in the urban school than at the suburban site. Personal concentrations were also strongly correlated

with indoor concentrations (except for O3). The responses to the questionnaire indicated that interactions with

tobacco smoke, solvent-based products and proximity to petrol stations increased exposure to pollutants. The

authors found a higher risk of cancer in the urban school group, particularly for children whose parents smoked,

than in children in the suburban location.

The risk assessment in this study focused on chronic exposure to pollutants rather than acute toxic effects. The

findings show that levels of pollution can differ significantly in different parts of a city, as can the health effects

of ambient and indoor exposure on children in different areas.

A number of studies on prenatal exposure to air pollutants indicate associations with cancer. A study

in California, USA, of more than 3000 children with various types of cancer (13) found a clear

relation between exposure to traffic pollution during gestation and the first year of life and the risk of

cancer by the age of 6 years, not only for acute lymphoblastic leukaemia but also for germ-cell

79

tumours and retinoblastoma. A study in Texas, USA, indicated an increased risk of embryonal

tumours in children whose mothers lived < 500 M from a major roadway during pregnancy (14). The

strongest association was found with retinoblastoma, the risk for which was increased 2.57 times. In a

study of more than two million children followed-up from birth to 4 years of age in Canada (15),

prenatal exposure to AAP, particularly during the first trimester of pregnancy, was associated with

increased risks of astrocytoma and acute lymphatic leukaemia. In another study in California, USA

(16), each 25 parts per billion increase in average maternal exposure to NOx during pregnancy

increased the risk for leukaemia in their offspring by 23%. Bilateral retinoblastoma was associated

with exposure to NOx during the second and third trimesters of pregnancy. Exposure to PAHs during

pregnancy was associated with a 1.44-times increase in risk of medulloblastoma in early childhood

(17).

The exposures of both parents must be taken into account in assessing the risk of childhood cancer, in

addition to exposure in utero (4). In a case–control study in Australia (18), both maternal exposure

during pregnancy and paternal pre-birth occupational exposure to diesel and petrol exhaust were

associated with an increased risk of acute childhood lymphoblastic leukaemia. A study in the United

Kingdom found a small but statistically significant increased risk of leukaemia in children whose

fathers were occupationally exposed to vehicle exhaust fumes and particulate hydrocarbons around

the time of conception (19).


Emissions from household combustion of coal have been classified by IARC as a Group 1 carcinogen

(20), emissions from household combustion of biomass fuel, in particular wood, are probably

carcinogenic, and the combustion of wood and other biomass fuels can produce toxicants including

CO, PAHs, aldehydes and free radicals that are classified as Group 2A carcinogens (probably

carcinogenic to humans). Metal compounds present in solid fuel emissions, such as arsenic and

nickel, have also been classified as Group 1 carcinogens (21). In some rural areas of China, up to the

60% of the population under 30 years of age are exposed to arsenic from household coal combustion,

which may account for the higher incidence of cancer in these populations (22).

HAP has been strongly associated with several types of cancer in adults, including lung cancer, upper

aerodigestive tract cancer, kidney and cervical cancer (20, 23–27), but few studies have been

conducted on HAP and cancer risk in children. A study in Australia (28) found increased risks of

childhood leukaemia by 1.41 times in association with use of a wood burner to heat the home during

pregnancy and by 1.25 times when used after birth. A case–control study in California, USA,

provided evidence of an association between lung cancer and exposure to coal-burning during

childhood and adolescence (29). Given the susceptibility of children, the known cancer risks of adults

and the longer time available for cancer to develop in children, further research should be conducted

on the risk of cancer associated with exposure to HAP during childhood.


The pathogenesis of childhood cancer is complex, as it involves many genetic and environmental

factors. In most cases, the primary causes remain unknown, although most of the scientific literature

suggests that the immune system plays a role. A prominent hypothesis is that faulty functioning of

the immune system in response to infections and allergies is the primary cause (8), and this is

supported by some studies that suggest that environmental exposure to certain chemicals and

pollutants that are known to alter the immune system can lead to this aberrant response and,

therefore, to the outcome of leukaemia (30).

It has also been proposed that air pollutants contribute to carcinogenesis by damaging DNA.

Particulate matter contains several genotoxic and mutagenic chemicals that cause single-strand

breaks, micronuclei, sister chromatid exchange and oxidative DNA damage mediated by reactive

oxygen species (31). DNA adducts and micronuclei have been identified in the cord blood of women

exposed to air pollution during pregnancy; these are important biomarkers of DNA damage that can

result in mutations leading to cancer (32). Furthermore, several studies have found that certain genes

in xenobiotics pathways (e.g. CYP2E1, GSTM1, NQO1, NAT2 and MDR1) increase the risk of

leukaemia by themselves or in association with exposure to chemicals (33).

80

Conclusion

The rising rate of cancer in children worldwide is deeply concerning. There is ample evidence that

both prenatal and childhood exposure to AAP is associated with increased risk of leukaemia and

other cancers. There is robust evidence of an increased risk for cancer in adults exposed to HAP, but

few studies have examined the association between HAP and childhood cancers.


• Few studies have been conducted on the association between exposure to HAP from polluting

fuels and childhood cancers.

References – cancer

1. Steliarova-Foucher ECM, Ries LAG, Hesseling P, Moreno F, Shin HY, Stiller CA. Lyon: International

incidence of childhood cancer. Vol. III. Lyon: International Agency for Research on Cancer; 2017.

2. IICC-3, International incidence of childhood cancer. Vol. 3, Results, introduction. Lyon: International

Agency for Research on Cancer; 2016 (http://iicc.iarc.fr/results/, accessed August 2018).

3. International Childhood Cancer Day: questions & answers. Geneva: World Health Organization; 2016

(http://www.who.int/cancer/media/news/Childhood_cancer_day/en/, accessed 26/3/2018).

4. Diesel and gasoline engine exhausts and some nitroarenes. IARC Monographs on the Evaluation of

Carcinogenic Risks to Humans, Vol. 105. Lyon: International Agency for Research on Cancer; 2014.

5. Benzene. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 100F. Lyon:

International Agency for Research on Cancer; 2012.

6. Benzene. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 120. Lyon:

International Agency for Research on Cancer; 2018.

7. Reşitoğlu İA, Altinişik K, Keskin A. The pollutant emissions from diesel-engine vehicles and exhaust

aftertreatment systems. Clean Technol Environ Policy. 2015;17:15–27.

8. Metayer C, Dahl G, Wiemels J, Miller M. Childhood leukemia: a preventable disease. Pediatrics.

2016;138:S45–55.

9. Boothe VL, Boehmer TK, Wendel AM, Yip FY. Residential traffic exposure and childhood leukemia: a

systematic review and meta-analysis. Am J Prev Med. 2014;46:413–22.

10. Carlos-Wallace FM, Zhang L, Smith MT, Rader G, Steinmaus C. Parental, in utero, and early-life

exposure to benzene and the risk of childhood leukemia: a meta-analysis. Am J Epidemiol. 2016;183:1–

14.

11. Spycher BD, Feller M, Röösli M, Ammann RA, Diezi M, Egger M, et al. Childhood cancer and residential

exposure to highways: a nationwide cohort study. Eur J Epidemiol. 2015;30:1263–75.

12. Demirel G, Ozden O, Döğeroğlu Y, Gaga EO. Personal exposure of primary school children to BTEX,

NO₂ and ozone in Eskişehir, Turkey: relationship with indoor/outdoor concentrations and risk assessment.

Sci Total Environ. 2014;473(4):537–48.

13. Heck JE, Wu J, Lombardi C, Qiu J, Meyers TJ, Wilhelm M, et al. Childhood cancer and traffic-related air

pollution exposure in pregnancy and early life. Environ Health Perspect. 2013;121:1385–91.

14. Kumar SV, Lupo PJ, Pompeii LA, Danysh HE. Maternal residential proximity to major roadways and

pediatric embryonal tumors in offspring. Int J Environ Res Public Health. 2018;15:505.

15. Lavigne E, Bélair MA, Do MT, Stieb DM, Hystad P, van Donkelaar A, et al. Maternal exposure to

ambient air pollution and risk of early childhood cancers: a population-based study in Ontario, Canada.

Environ Int. 2017;100(3):139–47.

16. Ghosh JKC, Heck JE, Cockburn M, Su J, Jerrett M, Ritz B. Prenatal exposure to traffic-related air

pollution and risk of early childhood cancers. Am J Epidemiol. 2013;178:1233–9.

17. von Ehrenstein OS, Heck JE, Park AS, Cockburn M, Escobedo L, Ritz B. In utero and early-life exposure

to ambient air toxics and childhood brain tumors: a population-based case-control study in California,

USA. Environ Health Perspect. 2016;124:1093–9.

18. Reid A, Glass DC, Bailey HD, Milne E, Armstrong BK, Alvaro F, et al. Parental occupational exposure to

exhausts, solvents, glues and paints, and risk of childhood leukemia. Cancer Causes Control.

2011;22:1575.

19. McKinney PA, Fear NT, Stockton D. Parental occupation at periconception: findings from the United

Kingdom Childhood Cancer Study. Occup Environ Med. 2003;60:901–9.

20. Household use of solid fuels and high-temperature frying. IARC Monographs on the Evaluation of

Carcinogenic Risks to Humans, Vol. 95. Lyon: International Agency for Research on Cancer; 2010.

21. Gordon SB, Bruce NG, Grigg J, Hibberd PL, Kurmi OP, Lam KH, et al. Respiratory risks from household

air pollution in low and middle income countries. Lancet Respir Med. 2014;2:823–60.

81

22. Millman A, Tang D, Perera FP. Air pollution threatens the health of children in China. Pediatrics.

2008;122:620–8.

23. Bruce N, Perez-Padilla R, Albalak R. Indoor air pollution in developing countries: a major environmental

and public health challenge. Bull World Health Organ. 2000;78(9):1078–92.

24. Kleinerman R, Wang Z, Wang L, Metayer C, Zhang S, Brenner AV, et al. Lung cancer and indoor

exposure to coal and biomass in rural China. J Occup Environ Med. 2002;44:338–44.

25. Lissowska J, Bardin-Mikolajczak A, Fletcher T, Zaridze D, Szeszenia-Dabrowska N, Rudnai P. Lung

cancer and indoor pollution from heating and cooking with solid fuels: the IARC international multicentre

case–control study in Eastern/Central Europe and the United Kingdom. Am J Epidemiol.

2005;162(4):326–33.

26. Hosgood HD 3rd, Wei H, Sapkota A, Choudhury I, Bruce N, Smith KR, et al. Household coal use and

lung cancer: systematic review and meta-analysis of case–control studies, with an emphasis on geographic

variation. Int J Epidemiol. 2011;40(3):719–28.

27. Duan X, Zhang J, Adair-Rohani H, Bruce N, Solomon H, Smith KR. Indoor air quality guidelines:

household fuel combustion. Review 8: Household coal combustion: unique features of exposure to

intrinsic toxicants and health effects. Geneva: World Health Organization; 2014.

28. Bailey HD, de Klerk NH, Fritschi L, Attia J, Daubenton JD, Armstrong BK, et al. Refuelling of vehicles,

the use of wood burners and the risk of acute lymphoblastic leukaemia in childhood. Paediatr Perinat

Epidemiol. 2011;25:528–39.

29. Wu AH, Henderson BE, Pike MC, Yu MC. Smoking and other risk factors for lung cancer in women. J

Natl Cancer Inst. 1985;74:747–51.

30. Wiemels J. Perspectives on the causes of childhood leukemia. Chem Biol Interact. 2012;196:59–67.

31. Valavanidis A, Fiotakis K, Vlachogianni T. Airborne particulate matter and human health: toxicological

assessment and importance of size and composition of particles for oxidative damage and carcinogenic

mechanisms. J Environ Sci Health C. 2008;26(4):339–62.

32. Pedersen M, Wichmann J, Autrup H, Dang DA, Decordier I, Hvidberg M, et al. Increased micronuclei and

bulky DNA adducts in cord blood after maternal exposures to traffic-related air pollution. Environ Res.

2009;109(8):1012–20.

33. Brisson GD, Alves LR, Pombo-de-Oliveira MS. Genetic susceptibility in childhood acute leukaemias: a

systematic review. ecancermedicalsci. 2015;9. https://doi.org/10.3332/ecancer.2015.539.

5.8 Later health outcomes

Key findings:

• Exposure to air pollution early in life can impair lung development, reduce lung function and

raise the risk of chronic lung disease in adulthood.

• Evidence suggests that exposure to air pollution during pregnancy can predispose the offspring

to cardiovascular disease later in life.

The life course of children can be significantly affected by exposure to toxic air pollutants. Children

who are exposed to air pollution during the prenatal period and early life are more likely to

experience adverse health outcomes as they mature and throughout adulthood. Exposure not only has

a direct impact on children’s health and development but can also stimulate latent diseases to become

evident only in later life. Air pollution can contribute to effects on all of the organs and systems of

the human body. Children’s physiological vulnerability and susceptibility to pollutants and the

delayed emergence of certain adverse effects are an area of growing scientific interest. Several recent

studies have addressed these factors and associated diseases.

Impairment of lung growth and development in childhood is an important risk factor for chronic lung

disease in adulthood. A study of two European cohorts (n=12 862, age 28–73 years) (1) showed that

exposure in early life was significantly associated with decreased FEV1 in adulthood, and the

estimates were almost as large as those for personal smoking. In a study of a Swedish cohort, 2278

children were followed up (2). It was found that exposure to traffic-related air pollution in infancy is

associated with a lower FEV1 at the age of 16 years. Factors in early life predicted decreased lung

function decades later, suggesting that some mechanisms related to lung ageing may be established in

childhood or in utero.

One of the common outcomes of impaired lung function is chronic obstructive pulmonary disease,

and the link between HAP and development of this disease has been addressed. A meta-analysis

82

found that people with long-term exposure to HAP from solid fuel combustion had twice the risk of

chronic obstructive pulmonary disease (3). Exposure to AAP and HAP also plays a role in the

development of lung cancer, as emissions from the combustion of solid fuels in the home and traffic-

related pollution both contain well-known carcinogens (4).

Exposure to high levels of air pollutants during pregnancy can predispose to cardiovascular diseases

later in life. Exposure to pollution as the fetal organs develop during pregnancy can trigger

susceptibility to weight gain and neuroinflammation in adulthood (5, 6). Early exposure to air

pollutants has also been associated with early cardiovascular phenotypes in young adults. Zhang et al.

(7) reported an association between higher exposure to PM2.5 during the third trimester of pregnancy

and high blood pressure in children at 3–9 years of age. Breton et al. (8) found an association

between prenatal exposure to ambient pollutants (PM10, PM2.5) and higher carotid arterial stiffness, a

biomarker of endothelial function, in a population of university students. Iannuzzi et al. (9) evaluated

52 Italian children and found that those who lived closer to a main road had higher carotid arterial

stiffness than those living further away. Thiering et al. (10) concluded that traffic-related air pollution

may increase the risk of insulin resistance. In all these studies, confounding variables such as

economic status, exposure to environmental tobacco smoke, onset of puberty and height and weight

were accounted for.

Honda et al. (11) reported a probable relation between early exposure to air pollutants and

development of anaemia later in life. This disorder is highly prevalent in elderly populations and is

associated with numerous adverse health outcomes.

Conclusion

There is increasing suggestive evidence that exposure to air pollution early in life can influence the

development of chronic lung disease, cardiovascular disease and other adverse health outcomes in

adulthood. Early exposure can sow the seeds of serious long-term illness, in addition to heightening

the risks of adverse outcomes in childhood. Thus, preventive measures to reduce exposure are likely

to be extremely cost-effective in terms of reducing the overall burden of disease in populations.

References – later health outcomes

1. Dratva J, Zemp E, Dharmage SC, Accordini S, Burdet L, Gislason T, et al. Early life origins of lung

ageing: early life exposures and lung function decline in adulthood in two European cohorts aged 28–73

years. PLoS One. 2016;11(1):e0145127.

2. Schultz ES, Hallberg J, Bellander Y, Bergström A, Bottai M, Chiesa F, et al. Early-life exposure to traffic-

related air pollution and lung function in adolescence. Am J Resp Crit Care Med. 2015;193(2):171–7.

3. Gordon SB, Bruce NG, Grigg J, Hibberd PL, Kurmi OP, Lam KBH, et al. Respiratory risks from

household air pollution in low and middle income countries. Lancet Resp Med. 2014;2(10):823–60.

4. Diesel and gasoline engine exhausts and some nitroarenes. IARC Monogrpahs on the Evaluation of

Crcinogenic Agents to Humans, Vol. 105. Lyon: International Agency for Research on Cancer; 2014.

5. Bolton JL, Smith SH, Huff NC, Gilmour IM, Foster WM, Auten RL, et al. Prenatal air pollution exposure

induces neuroinflammation and predisposes offspring to weight gain in adulthood in a sex-specific

manner. FASEB J. 2012;26(11):4743–54.

6. Backes CH, Nelin T, Gorr MW, Wold LE. Early life exposure to air pollution: How bad is it? Toxicol

Lett. 2013;216(1):47–53.

7. Zhang M, Mueller N, Wang H, Hong X, Appel H, Wang X. Maternal exposure to ambient particulate

matter ≤ 2.5 µm during pregnancy and the risk for high blood pressure in childhood. Hypertension.

2018;72(1):194–201.

8. Breton CV, Mack WJ, Yao J, Berhane K, Amadeus M, Lurmann F, et al. Prenatal air pollution exposure

and early cardiovascular phenotypes in young adults. PLoS One. 2016;11(3): e0150825.

9. Iannuzzi A, Verga MC, Renis M, Schiavo A, Salvatore V, Santoriello C, et al. Air pollution and carotid

arterial stiffness in children. Cardiol Young. 2010;20(2):186–90.

10. Thiering E, Cyrys J, Kratzsch J, Meisinger C, Hoffmann B, Berdel D, et al. Long-term exposure to traffic-

related air pollution and insulin resistance in children: results from the GINIplus and LISAplus birth

cohorts. Diabetologia. 2013;56(8):1696–704.

11. Honda T, Pun VC, Manjourides J, Suh H. Anemia prevalence and hemoglobin levels are associated with

long-term exposure to air pollution in an older population. Environ Int. 2017;101(4):125–32.

83

6. Recommended actions for health professionals

Air pollution is a global problem. Evidence of its negative health effects – which may have both

lifelong and generational impacts – is clear and compelling.

The developing fetus and child are particularly vulnerable to the effects of air pollution and are at risk

of both short- and long-term health outcomes. As summarized above, numerous studies have linked

air pollution to adverse birth outcomes, infant mortality, neurodevelopmental disorders, childhood

obesity, compromised lung function, pneumonia, asthma and otitis media, with associations of

varying strength. In light of this evidence, major health professional organizations throughout the

world are focusing increasingly on the adverse health impacts of air pollution on children. While

further research is needed in a number of areas, the scientific evidence is already sufficient for taking

clear, concrete steps now to reduce the exposure of pregnant women and children to air pollution.

Health professionals are trusted sources of information and guidance. Paediatricians, family doctors,

gynaecologists, obstetricians, midwives, nurses and community health care workers who interact with

children can all play significant roles in advocating for policies to reduce childhood exposure to air

pollution. Health care professionals commonly treat the effects of exposure-related illness but rarely

receive training in identifying and managing the underlying causes and are rarely involved in policy-

making. Health professionals should expand their role in the management of childhood exposure to

air pollution, with better methods of care and prevention and collective action.

The broader health sector must develop a comprehensive approach to this problem. Preventing the

health impacts of air pollution on children requires action by both decision-makers and individual

health care professionals, who are best positioned to educate both the public and policy-makers about

the dangers of air pollution and to suggest the most promising solutions (Fig. 14).

Fig. 14. Critical roles of health professionals

Be informed.

Recognize exposure and the associated health conditions.

Conduct research and publish and disseminate knowledge. Prescribe solutions and educate families and communities. Educate colleagues and students.

Advocate to policy- and decision-makers.

6.1 Be informed.

Health professionals should be aware of the sources and patterns of air pollution in their communities

and any tools that can be used to monitor air quality. Regulatory levels of air pollutants are

established in almost all major cities. When these regulatory levels are exceeded, health professionals

should be prepared and know what action to take to protect the health of their patients. All health

professionals should understand the sources of environmental exposure in the communities they serve

and should consider air pollution a major risk factor for their patients. They should remain aware of

the existing and emerging evidence on the ways in which air pollution can affect children’s health.

6.2 Recognize exposure and associated health conditions.

Health professionals are trained to prevent, detect, diagnose and treat health conditions. They also

have an important role in identifying causative risk factors in order to prevent disease. Training in the

prevention of early childhood exposure will reduce not only common childhood morbidity but also

adult mortality. A health care provider can identify air pollution-related risk factors by asking

pertinent questions about the child’s or pregnant mother’s environment. Primary health and

community workers can take the opportunity to observe and assess exposure during home visits or

when providing advice on infant feeding and during visits to schools and community centres.

Questions can be asked during a medical visit to evaluate the risk of exposure to hazardous air

pollutants. Box 16 provides examples of questions that could be asked. For more specific guidance

on evaluating environmental risks associated with air quality, see Children’s health and the

environment: a global perspective (1). Primary health and community workers can take the

opportunity to ask questions about the child’s environment during Integrated Management of

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Childhood Illnesses. Alternatively, a comprehensive environmental risk assessment can be conducted

during consultations with pregnant women or children who present with air pollution-related health

effects, to assess and understand their current exposure and prevent further exposure. A concise

version of taking a paediatric environmental history has been prepared and has been field-tested in

Argentina; it is available with guidance materials on the WHO website

(http://www.who.int/ceh/capacity/paedenvhistory/en/). The more questions that are asked about the

child’s environment, the more valuable the information collected, as it allows health professionals to

identify causative risk factors for acute, recurrent and chronic conditions and helps them educate

families on preventing further exposure. Box 17 gives examples of questions that can be asked to

determine the risk of AAP.

Box 16. Examples of clinical questions for determining household air pollution risk (2–4) Cooking

1. What fuel does this household use for cooking (including cooking food, making tea/coffee and boiling

drinking-water)? Please circle all cookstoves or devices used. If any technologies are used that are

associated with health risk, explain that these stoves produce high levels of pollution that is harmful to

health.

No cooking done in household······································· 0 SKIP to Q 5

Electric stove·······························································1 CLEAN FOR HEALTH SKIP to

Q.5

Solar cooker································································2 CLEAN FOR HEALTH SKIP to

Q.5

Piped natural gas stove··················································· 3 CLEAN FOR HEALTH SKIP to

Q.5

Biogas stove································································4 CLEAN FOR HEALTH SKIP to

Q.5

Liquefied petroleum gas (LPG)/ cooking gas stove···········……..5 CLEAN FOR HEALTH SKIP to

Q.5

Liquid fuel stove:

···· Using alcohol / ethanol·············································· 6 CLEAN FOR HEALTH SKIP to

Q.5

···· Using gasoline / diesel···············································7 HEALTH RISK

···· Using kerosene/paraffin·············································8 HEALTH RISK

Manufactured/artisanal solid fuel stove that meets standards for “advanced” (ISO Tier 4 or 5)······· 9

CLEAN FOR HEALTH SKIP to Q.5

Manufactured / artisanal solid fuel stove (ISO Tier 0–3)··· ……...10 HEALTH RISK

Traditional solid fuel stove·············································· 11 HEALTH RISK

Three stone stove/open fire··········································…· 12 HEALTH RISK

If the household uses polluting fuels or stoves for cooking (options with a HEALTH RISK), ask these follow-up

questions:

2. Where is cooking usually done? The exposure of the cook and others is greatest when cooking is done in

the main house. Cooking with polluting fuels or stoves can release high concentrations of air pollution.

Cooking outdoors or in areas with good ventilation may reduce exposure to air pollution.

In main house: no separate room··································· 1

In main house: separate room········································ 2

Outside main house: in a separate room····················· 3

Outside main house: in open air·································· 4

On veranda or covered porch········································· 5

3. Does the cookstove have a chimney or a hood? If yes, this can reduce the air pollution from cooking or

heating.

Yes···········································································1 CAN REDUCE EXPOSURE

No................................................................................... 2

Don’t know..................................................................... 3

4. Does your child / do your children spend time around the cookstove or fire? If yes, the child can be

exposed to high levels of harmful air pollution. It is suggested that children minimize the time spent in

areas where cooking is done if polluting stoves or fuels are used.

Yes·················································································· 1 HEALTH RISK

No................................................................................... 2 CAN REDUCE EXPOSURE

Don’t know..................................................................... 3

Space-heating and other energy uses

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5. What fuel does this household use for heating? For example, do you use a space heater(s) or your

cookstove for warmth? Please circle all space-heating devices used. If any of them are associated with a

health risk, explain that these devices produce high levels of pollution that is harmful to health.

No space heating in house······························· 0

Central heating······················································· 1 CLEAN FOR HEALTH

Heat pump····························································· 2 CLEAN FOR HEALTH

Manufactured space heater························· 3 POSSIBLY CLEAN FOR HEALTH

Traditional space heater or cookstove··············· 4 HEALTH RISK

Open fire··························································· 5 HEALTH RISK

Moveable heating pan······································ 6 HEALTH RISK

Three-stone stove or open fire··········································· 7 HEALTH RISK

6. Does your household burn wood, coal, charcoal, dung, kerosene or agricultural residues for cooking, heating,

lighting or other purposes in or near the home? For example, do you use kerosene lamps, biomass to cook food

for animals or burn crop residues to keep flies away from your animals?

If yes: explain that burning these fuels around the home releases high concentrations of air pollution

that can be harmful to the health of children.

Box 17. Examples of clinical questions for determining risk of ambient air pollution (2,4–8) 1. Do you identify or perceive sources of smoke, fog or dust close to your household? Examples include

fires from burning garbage or other residues, smoke, smog or dust from surrounding industrial or

agricultural activities.

........ usually see, smell, perceive smoke, dust or mist around the house....................... Health risk

.. ......burning areas from dumps and landfills.................................................................. Health risk

.........industrial or agricultural area ..................................................................................... Can reduce exposure

... ... Do not perceive smells, mist, smoke or dust around the house.............................. Clean for health

If any identified ambient air pollution is associated with a health risk, advocate for local monitoring and

control and suggest that parents minimize the time their children spend outside while pollution is present.

2. Does your child/your children live or spend time in an area with heavy traffic or a traffic-congested

area, such as a road with frequent blocked traffic, slower speeds and long queues?

.... rural or urban area with light traffic........... Clean for health

.. urban with heavy traffic and common traffic congestion.... Health risk

If traffic represents a health risk, advocate for local monitoring and control, and suggest that parents keep the

windows closed and minimize the time their children spend outside while traffic is congested.

3. How often do lorries pass through the street where you live, on weekdays?

Never……………….

Seldom………….

Frequently……….

Almost the entire day…….

Any other response……….

4. What is the distance (in meters) from your house to the nearest busy road with frequent traffic?

5. For how many hours a day does your child play outdoors?

6.3 Conduct research and publish and disseminate knowledge.

Environmental health literacy is increasing rapidly. Health professionals have a wealth of knowledge

available to them and are themselves trusted sources of synthesized knowledge. With growing

interest in air pollution and its health effects, health professionals are in a unique position to identify

causative risk factors, educate patients on prevention and advocate for protective interventions. As

noted throughout this publication, many gaps in research gaps remain. It is well established, however,

that children are the most vulnerable to environmental exposure because they have a lifetime ahead of

them for development of the associated illnesses. In order to better protect children from the

consequences of air pollution, better understanding is needed of the different sources of pollution,

how they enter the body and their biological effects. Health professionals must build collaborations,

86

work closely with affected communities and identify and evaluate potential interventions. The health

sector is well positioned to take the lead in narrowing the knowledge gaps.

Health professionals play a critical role in advancing research on the effects of air pollution on

children’s health, as they observe the effects in their daily work. Both independently and in

collaboration with researchers, health professionals can conduct and publish investigations of the

causes, mechanisms and effects of environmental exposure that affects children, as well as potential

treatment, prevention and management options. By publishing articles and submitting reports of

unique cases, they can help other practitioners to identify signs of air pollution-related health

outcomes and raise awareness of potential exposure pathways. Health professionals can also play a

valuable role in recruiting patients for large studies, because of their relationships with patients. They

also contribute to identification of public health concerns by reporting sentinel cases and clusters of

air pollution-related diseases to government authorities and can assist in monitoring and identifying

sources of pollution. Health professionals are encouraged to consider interventions that may improve

the lives of children exposed to air pollution and design pilot studies to determine their effectiveness

and efficiency in use of resources. It is also important that they use this evidence to inform social and

behaviour change communication strategies for public health promotion and prevention of exposure

of children to air pollution. Box 18 lists the priorities for research on air pollution and effects on

health.

Box 18. Research priorities

• Studies of the efficacy of personal protective devices (e.g. facemasks) have shown mixed results. Further

studies are required.

• Research on the effect of HAP on children’s health is limited. Few sources have been investigated, and the

available evidence does not provide a detailed assessment of health outcomes. The types of pollutants and

their effects both prenatally and during childhood periods should be evaluated.

• Not only epidemiological studies but also large intervention studies and implementation research are

required to assess the efficacy of interventions and their potential deployment on a larger scale.

• Although many studies have evaluated the effects of chemical mixtures in air pollution on children,

investigations of associations between chemical components and health effects will clarify which

pollutants are most dangerous and how they should be regulated.

• Long-term studies of the effects of air pollution on children over time are necessary to determine the

lasting effects of exposure. Children are vulnerable to environmental exposures partly because they have a

lifetime to develop illness. More research on health status in adulthood after childhood exposure to air

pollutants will indicate whether there is a link with chronic illness.

• There are few studies of interventions. As more becomes known about the effects of air pollution on

children’s health, studies of protective policies and patient treatments and interventions will be critical.

• There is increasing recognition that exposure to air pollution in early life can cause epigenetic changes.

More research is needed on the long-term consequences of such changes and their role in the biological

mechanisms for a wide range of health outcomes.

6.4 Prescribe solutions and educate families and communities.

Health professionals can “prescribe” solutions to problems related to air pollution, such as switching

to clean household fuels and devices to reduce exposure (Boxes 19 and 20). When it is difficult to

change to clean household energy, health care professionals can recommend “transitional” solutions

that offer some health benefit. Information could be collected on the availability, accessibility and

affordability of clean household energy alternatives and on the obstacles and also on resources and

information available in government and other programmes to help reduce exposure. Education and

individual protective measures, such as using clean stoves for cooking and clean-burning space

heaters, could mitigate HAP, often improving the health of the whole family. Reducing AAP,

however, requires action throughout the community: individual protective measures at family and

household level are important but often not enough. As entire communities are affected by AAP –

which in turn is determined by regional sources and meteorological patterns – policy interventions

are necessary. (See Boxes 20 and 21 for examples.)

87

Box 19. Advice that clinicians could give to patients.

Note: These are not official guidelines but suggested actions that patients can take to improve the quality of the

air and health and safety in their home environments.

• Use only clean household energy for cooking, heating and lighting.

• Use the cleanest possible solutions. As the transition to truly clean energy sources can take time,

technologies and fuels that reduce exposure the most should be used (e.g. low-emission biomass

cookstoves).

• To reduce the exposure of children to hazardous HAP, minimize the time children spend around smoky

fires and kerosene lamps.

• Increasing ventilation by opening windows or doors or installing a chimney with regular maintenance can

reduce exposure in the indoor environment.

• To reduce the risk of burns and scalds, ensure that technologies and fuels are used in such a way that it is

unlikely that they can be pushed over, dropped, handled or touched by children.

• To minimize the risk of poisoning, do not store liquid fuels in water bottles or similar beverage containers,

and keep them out of the reach of children.

• Ensure that stove and fuel combinations have appropriate safety controls and mechanisms (e.g. safety

valves) and are regularly maintained.

• Avoid tobacco smoking indoors.

• To reduce exposure during acute episodes of AAP, minimize children’s outdoor physical exertion and the

time they spend outdoors, especially in areas with heavy traffic. During these events, families should seek

advice from a medical doctor before letting their children participate in outdoor sports and other physical

activity.

Box 20. Messages for families and communities.


AAP is a risk factor for respiratory diseases in children, including reduced lung function, exacerbated

respiratory symptoms and increased severity or frequency of asthma attacks.

For families:

• Respect advisories on local air pollution. In some cities, air pollution can be so severe that people may be

advised to limit their activities and mobility, and schools might close. In many countries and regions,

advisories are becoming more interactive, with display boards in some locations that show the current

level of air pollution or indicate local air quality. Increasingly, mobile phones apps are used to forecast

local air quality.

• Be aware of the environment. Families can identify signs and symptoms in their child that may be

associated with local air pollutants, bring them to the attention of their health provider and promote

investigation.

• Work with the community. Families are encouraged to collaborate with other community members, health

providers and the government to identify air pollution in their area, take action to protect children from

exposure and contribute to policy-making.

For communities:

• Be aware of the effects that local activities and natural events have on air quality and the potential impact

on children’s health. Rural areas are affected by outdoor air pollution primarily from burning debris on

agricultural land and forest fires. With increasing desertification, dust storms may contribute substantially

to outdoor air pollution. Air pollution is influenced by regional wind and weather conditions and may be

transported over long distances.

• Take action to improve air quality whenever possible. Reduced exposure decreases the risk of health

effects. Communities should reduce health risks by identifying and reducing emissions from local sources

of pollution. For example, in urban areas, a significant proportion of air pollution is generated by old

vehicles, poor vehicle maintenance and low fuel quality. Communities may advocate to phase out these

vehicles.

• Become aware of new measures to reduce air pollution. Cost-effective strategies to reduce pollution

include better integration of transport and land use (e.g. high-capacity, dedicated busways and pedestrian

and cycle networks), use of cleaner (lead-free, low-sulfur) fuels, cleaner vehicle standards and

technologies, monitoring of air quality and warnings.

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• Actions to reduce air pollution will benefit child health, not only by avoiding direct effects but also by

reducing emissions of certain greenhouse gases and thus mitigating climate change and its effects on

health (5).

Household air pollution from use of polluting fuels

Use of polluting fuels for cooking and heating poses a serious threat to children’s health. HAP is associated

with adverse birth outcomes, increased infant mortality, deficits in childhood lung function, asthma and

increased risks for lung infection.

For families:

• Switching from wood, dung, coal, charcoal or kerosene to more efficient, less polluting fuels like

electricity, solar energy, LPG, biogas and ethanol will reduce exposure to harmful pollutants.

• Kerosene is not a clean fuel. Avoiding use of kerosene for cooking will also prevent burns and poisoning.

• Proper ventilation during cooking and heating may be a partial remedy. Installing eaves spaces and

extraction through smoke hoods and opening windows and doors can also reduce indoor air pollution.

• Changing behaviour plays an important role. Newborns and infants are often carried on their mothers’

backs while they are cooking or kept close to a warm hearth. Consequently, they spend many hours

breathing polluted air during their first years of life when their developing airways and their immature

immune systems make them particularly vulnerable. Pregnant women should also keep a distance from

such sources (9).

• Quit tobacco smoking or at least avoid smoking in the house (10).

For communities:

• Resources and information on relevant government and other programmes to reduce exposure in the home

should be easily accessible. Such programmes are often inexpensive and highly effective.

• Ensure that advice and support to family members to stop tobacco smoking are available to families.

Box 21. Children’s environmental health units – a resource for health professionals, communities and

families.

“Children’s environmental health units” are now established around the world to ensure the care and protection

of children exposed to environmental factors that may adversely affect their health. In view of the particular

vulnerability of children, the units are accumulating information on the dangers of air pollution and other

hazards, responding to public concern, training health professionals and educating communities, governments

and other sectors. They are dedicated to the protection of children from environmental threats, management of

children with known or suspected exposure to environmental hazards and diagnosis, management and treatment

of children with illnesses due to such exposure.

These units, also referred to as “paediatric environmental health units” have been set up in many countries since

they were initiated by WHO (11). For example, a unit has been working in Uruguay for more than a decade,

and a network of paediatric environmental health specialty units (https://www.pehsu.net/) are located in Canada

and the USA to promote awareness of environmental health issues, provide advice and guidance to reduce

exposure, help families to seek care and aid in the training of health professionals. There are also paediatricians

who run activities on children’s environmental health in medical centres, although there are still no national

policies to support such centres.

If you suspect that your child has symptoms related to environmental exposure or you are concerned about a

potential exposure and would like to discuss it with a professional, find a children’s environmental health unit

near you and talk to your health care provider about possible treatment. If no such unit exists, advocate for one

to be created. Units exist in many places, and their creation does not require extensive resources.

6.5 Educate colleagues and students.

By training others in health and education, health professionals can increase the reach of their

messages on the health risks of air pollution and on strategies to reduce exposure. Health

professionals can: • educate and engage their colleagues in the workplace, in local health care centres, at conferences

and in health professional associations;

• advocate for the inclusion of children’s environmental health and the environmental

determinants of health in curricula in post-secondary institutions and particularly in medical,

nursing and midwifery schools;

89

• engage student associations in the health and care professions (e.g. medicine and nursing); and

• promote reduction of air pollution in schools and environmental health education for teachers

and students.

Exposure to air pollution and the interventions to counteract it depend on the country of residence,

urban or rural location, socioeconomic situation and other factors. WHO training materials are

available that can be adapted to the location, situation and audience (see Box 22).

Box 22. Training materials for health professionals

To enable health professionals who care for children’s and adolescents’ health to recognize, assess and then

manage and prevent diseases linked to environmental factors, WHO and experienced professionals have

prepared a training package on children’s environmental health, including “train the trainer” materials and tools

(12). WHO promotes its wide use for training.

6.6 Advocate to policy- and decision-makers.

Health professionals around the world recognize that air pollution is a threat to the healthy

development of children. They should share their knowledge with decision-makers, including local

governments, school boards and community leaders. Health professionals can accurately convey the

health burden of air pollution to decision-makers, conduct health assessments, support improved

standards and policies to reduce harmful exposures, advocate for monitoring and emphasize the

importance of protecting children at risk.

The health sector is promoting this

message, coordinating with other groups

and aligning their work with government,

academia and community to ensure that

children are protected. Resources available

to support outreach and coordination

include the WHO training manual on

health-in-all policies (13) and the BreatheLife campaign (http://breathelife2030.org/). Health

professionals can also advocate; however, by engaging more comprehensively with the broader

health sector, they can extend and increase their influence. Promoting interventions and policies to

decrease exposure to air pollution during a child’s early years will contribute to lifelong health and

future well-being.

The heightened risks of children should lead health professionals to consider the “precautionary

principle”: when there is a likelihood of serious or irreversible damage to health, a lack of full

scientific certainty should not preclude the pursuit of effective preventive measures (14). The

American Public Health Association (15) and WHO (16) have proposed approaches for using the

precautionary principle to protect children from environmental risks such as air pollution. For some

of the health outcomes discussed above, there is strong evidence of the effects of AAP on child health

effects, but few studies of HAP. As AAP and HAP share many of the same types of combustion

sources, minimizing children’s exposure to both forms of pollution, especially during the most

sensitive developmental stages of early life, should take precedence over establishing near-certainty

about the full extent of the risk and the mechanisms involved (17).

6.7 Benefits of cleaner air for health and the climate

HAP and AAP contribute significantly to global climate change. Household energy is a source of

both CO2 and short-lived climate pollutants such as methane, black carbon and volatile organic

compounds. Short-lived climate pollutants are also emitted by diesel-fuelled vehicles and generators,

open burning of agriculture waste and livestock production (18). Some of the products of incomplete

combustion of biomass or fossil fuels contribute to the formation of O3, another potent climate

pollutant.

Black carbon and O3 damage children’s health in both the short term, by direct exposure, and the

longer term, by increasing food insecurity, extreme weather, water scarcity and infectious disease

incidence brought on by global climate change (19, 20). As atmospheric temperatures increase, so

will vector-borne disease, diarrhoeal diseases and undernutrition – some of the major killers of young

Health professionals around the

world recognize that air pollution is

a threat to the healthy development

of children.

http://breathelife2030.org/

90

children. Concentrations of ground-level O3, pollen, mould and other pollutants will increase as well,

exacerbating respiratory illness in children, such as asthma and allergies (21).

The relation between air pollution and climate change is complex: AAP contributes to climate

change, and climate change affects air quality. Rising temperatures can result in more frequent, more

severe smog and higher annual mean concentrations of PM2.5 in certain parts of the world.

The interconnected effects of HAP, AAP and climate change strengthen the benefits of emissions

reduction for health and the environment. Changing household energy use to clean options and

reducing AAP are critical to both improving children’s health and mitigating global climate change.

Box 23 gives examples of policy measures for which health professionals could advocate, Box 24

gives an example of a policy that was successful in reducing AAP, and Box 25 describes ways in

which households were convinced to change to cleaner fuels.

Box 23. Examples of policy measures for which health professionals could advocate (20)

Each of these measures can be promoted by stressing the health effects of air pollutants in children to policy-

makers.

• Accelerate access to clean and efficient household energy for cooking, heating and lighting. Programmes to

improve access to cleaner fuels and improved stoves can result in large gains in health and productivity and

are highly cost-beneficial. Health professionals can promote national campaigns for improved fuels and

technologies that adhere to the WHO guidelines for indoor air quality associated with household fuel

combustion (22).

• Reduce the emissions of harmful ambient air pollutants from major sources such as heavy vehicles,

through grants and low-cost loan programmes. For example, the Diesel Emissions Reduction Act in the

USA provides funds to retrofit or replace diesel buses and other vehicles that emit high levels of NOx, PM

and other pollutants.

• Encourage land-use planning for energy-efficient, compact cities that shorten distances, encourage urban

street life and create opportunities for children to play and interact with their community.

• Support investments in “green” and “blue” spaces, such as parks, forests and lakes, so that children can

benefit from green areas and clean waters. These can also provide urban ecosystem services, including a

cooling effect and a refuge from air and noise pollution.

• Transfer subsidies from polluting fuels such as kerosene to clean household options (e.g. solar, biogas,

LPG).

• Design a labelling system for stoves and space heaters that includes rating of emissions (linked to health

impacts) and efficiency, perhaps using the ISO standards for voluntary performance targets for clean

cookstoves and clean cooking solutions (23).

• Encourage further installation of low-emission and renewable power generation to reduce AAP.

• Support public investment in rapid urban transit with dedicated rights of way and pedestrian and cycle

networks. Support use of public transport, bicycles, walking and programmes such as safe routes to school

and children’s free access to public transport in order to increase children’s independent mobility.

• Support the planning and building of energy-efficient housing, clustered in neighbourhoods with schools,

shops and services nearby. Upgrade slums by introducing cleaner, safer street networks, larger green

spaces and better infrastructure to improve children’s physical living conditions and quality of life.

• Discourage urban planning that results in low-density urban sprawl and expanding roads, gated

communities and large expanses of concrete, which absorb heat and block sunlight, all of which discourage

children’s involvement in urban street life.

• Advocate for better waste management practices to reduce incineration and burning of agricultural waste

and for phasing out use of agro-chemicals in or near urban areas.

• Promote better land-use management in rural areas to stop deforestation, limit agricultural burning

practices and charcoal production and improve control of wildfires.

• Create an emergency alert system that provides information on air pollution resulting from natural disasters

such as thunderstorms and wildfires, and advocate for protective measures in such situations.

Box 24. A success story in improving air quality

This case study demonstrates what can be achieved with a coordinated, effective policy. Southern California

used to be one of the most polluted regions in the USA. A persistent brown haze was a fact of life for residents

of Los Angeles in the 1960s. Over the past few decades, however, California has cleaned up its outdoor air

dramatically. The state treated its AAP problem as a public health crisis that demanded strong action. It

introduced strict emission controls on almost every source of AAP, with low-emission vehicle programmes,

emissions standards for heavy-duty vehicles and diesel vehicles, requirements for emissions reduction by power

91

plants and refineries and programmes to improve the energy efficiency of and reduce emissions from consumer

products and appliances. Through the Clean Air Act and its amendments, the Federal Government also imposed

more stringent air pollution standards and effective, concurrent policies to reduce emissions from vehicles and

energy-related industries. California’s ambitious efforts, however, went beyond the standards set by national

legislation and had huge benefits for Californians, as the concentrations of several air pollutants were reduced

by 15–65% (24). Children in California now breathe more easily, as the reduction in pollutant levels has

resulted in significant improvements in the lung function (25). California lawmakers took action partly in

response to strong evidence of the effects of air pollution on health, highlighting the important role of health

professionals in fostering change.

Box 25. Smart subsidies: success in transitioning millions of households to clean fuels

The aim of “Cooking for life”, a project of the World LPG Association and the United Nations, is to convince

households to change from polluting fuels for cooking primarily to LPG, a fuel that is much cleaner for health.

Such a change requires access to affordable fuels and technologies and the financial means to obtain them, and

these factors can be influenced by public policy (26).

Indonesia’s successful programme for increasing LPG use offers good lessons. In 2006, 48 of 52 million

households in Indonesia were using kerosene, a polluting fuel that is also a major risk of poisoning in children,

as their main energy source. The Government subsidized the purchase of kerosene, at major expense to the

State coffers. In 2007, the Government launched a national programme to switch more than 50 million people

from kerosene to LPG. After an extended assessment involving authorities at all levels, conversion packages

were distributed, accompanied by communication and education campaigns to increase awareness. Within the

first 6 years, LPG packages had been distributed to 54 million households. A large proportion of households

have since switched to this cleaner energy source, reducing the cost to the Government of petroleum-related

subsidies by more than US$ 6 billion. The programme resulted in a reduction in CO2 emissions by 8.4 million

tonnes per year and also reduced emissions of other air pollutants, such as PM, methane, CO and hydrocarbons.

A survey after implementation of the policy showed that 99.8% of households preferred using LPG to kerosene,

citing greater efficiency, speed of cooking and cleanliness (27).

Children’s health improved dramatically: the infant mortality rate fell by 30% in the regions that received the

intervention, half of the decrease being seen among infants in the early neonatal period. This carefully

coordinated nationwide intervention is a compelling example for other countries that wish to accelerate the

transition from household use of polluting fuels to benefit the health of their children (27–29).

Further evidence of the importance of targeted subsidies in promoting a transition to clean fuels was found in

Latin America and the Caribbean. Use of LPG varies widely across the region, and over 80 million people still

rely on polluting fuels as their primary source of household energy. Achieving SDG 7 (“ensure access to

affordable, reliable, sustainable and modern energy for all”) would have huge benefits for health in the region,

as it would avert an estimated 82 361 premature deaths and 2 327 146 DALYs annually (30). In Latin America

and the Caribbean, access and price were identified as the main limitations to substitution of solid fuels by clean

fuels. Subsidies for LPG consumption helped Bolivia to lower the proportion of the population that used

polluting cooking fuels from 36% in 2005 to 23% in 2013. Other countries, such as Brazil, used cash transfer

programmes, which accelerated the transition, from 16% to < 5% in a decade. It has been estimated that

subsidies for natural and LPG gas allowed 39% of the rural population and almost the entire urban population

to switch from polluting fuels to LPG. Targeted subsidies should therefore be considered a policy option for

achieving the SDG7 on clean energy.

Effective programmes to promote adoption of clean fuels can result in meaningful health gains for children.

Health professionals can play a role, by justifying the health benefits of such interventions to policy- and

decision-makers, drawing on these success stories.

6.8 A perspective on children’s health and air pollution: improving equity and access to

protect the most vulnerable

While providing information to families and communities is important, it may not remove other

barriers to access. The poorest communities often face the greatest environmental risks to their

health, and their children are especially vulnerable. Poverty often underlies these disproportionate

risks, as those living in poor communities may be unable to afford cleaner household fuels, access

care or move to an area with cleaner air. They may also be members of marginalized groups that face

systemic barriers to reducing exposure to air pollution. Polluting industries, waste disposal sites, bus

depots and trucking routes are often situated in low-income communities, where residents lack the

political power to limit or remove these harmful sources of exposure.

92

In households that rely on polluting fuels for cooking and heating, women and their children often

travel long distances to collect fuel and spend much time gathering, processing and cooking with

these fuels. Removing or reducing the primary sources of pollution is often the best way to protect

children from the damage it can cause. Therefore, low-cost solutions and approaches are likely to

result in the most progress in affected communities.

Low-income families are often constrained in their options to improve air quality in their own homes.

Because of market and other forces beyond their control, clean fuels and technologies may not be

affordable, available or accessible. Beyond the household, individuals and families have even less

control over the air they breathe. To reduce and prevent exposure to both HAP and AAP, public

policy is essential. Evidence-based air quality standards, monitoring pollutant levels over time and

proper enforcement mechanisms can reduce exposure and save lives.

Air pollutants have no political borders; they travel where the wind and prevailing weather patterns

take them. Therefore, regional and international cooperation are also necessary to reduce children’s

exposure. Like the pollutants, actions to protect children should transcend sectoral, geographical and

political boundaries. What is needed are approaches to preventing exposure that are complementary

and mutually reinforcing, at every scale: in the home, the clinic, the health care institution, the

municipality, the national government and the global community. Collectively, health care

professionals can push for strong action from decision-makers to protect their most vulnerable and

voiceless citizens: children who have little or no control over the quality of the air that surrounds

them. Exposure to air pollution can alter children’s trajectory through life, pushing them onto a path

with suffering, illness and challenge. In some cases, the damage is irreversible – but it is also

preventable. The sources of risk and the health effects are diverse and complex, but the solution is

clear: we must reduce children’s exposure. By working together, we can protect millions of

vulnerable young lives from the health effects of air pollution. The benefits would be immeasurable.

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94

Annex 1. Global initiatives and organizations working on children’s health

and air pollution

Sixty-eighth World Health Assembly (2016)

A 4-year plan for responding to the adverse health effects of air pollution that includes four areas of

action:

1. Expand the knowledge base, by building and disseminating global evidence and knowledge on

the effects of air pollution on health and the effectiveness of interventions and policies to address

it.

2. Enhance systems to monitor and report on health trends and progress towards the air pollution-

related targets of the Sustainable Development Goals.

3. Leverage health sector leadership and coordinated action at all levels – local, national, regional

and global – to raise awareness of air pollution.

4. Enhance the health sector’s capacity to address the adverse health effects of air pollution by

training, guidelines and national action plans.

http://www.who.int/mediacentre/news/releases/2016/wha69-27-may-2016/en/

http://www.who.int/sustainable-development/news-events/wha69-roadmap-ap/en/

Sustainable Development Goals (2015)

Health and air pollution are addressed throughout the SDGs. The third SDG, “ensure healthy lives

and promote well-being for all at all ages”, emphasizes children’s environmental health. Household

energy is explicitly addressed in SDG 7: “ensure access to affordable, reliable, sustainable and

modern energy for all”. The SDG targets reflect understanding that household energy is a critically

important consideration in many facets of human development, from health (SDG 3) to sustainable

urban environments (SDG 11) to gender equality (SDG 5) and climate action (SDG 13). SDG 3

includes targets to end preventable deaths of newborns and children under 5 years by 2030, which

form the basis of the Global Strategy for Women’s, Children’s and Adolescent’s Health.

http://www.un.org/sustainabledevelopment/sustainable-development-goals/

WHO’s Global Strategy for Women’s, Children’s and Adolescents’ Health (2015)

The strategy has been updated by collaboration among stakeholders, led by WHO. It places women,

children and adolescents at the heart of the Sustainable Development Goals. Its aims include reducing

premature mortality from noncommunicable diseases in women, children and adolescents by one

third by 2030. The Strategy includes targets for extending access to clean household energy and

reducing HAP. http://www.who.int/life-course/partners/global-strategy/en/

WHO Nurturing Care Framework for Early Childhood Development (2018)

The Framework, launched at the Seventy-first World Health Assembly in May 2018, outlines a

whole-of-society approach to ensure effective policies and interventions for a stable, supportive

environment and protection from threats to health and well-being. Environmental risks are identified

as clear threats to early childhood development, and tools are proposed to translate scientific results

into action. The Framework represents the next step in a global movement to prioritize and invest in

early childhood development. It provides an opportunity for more action to address air pollution and

other environmental risks as part of broader child health programmes and policies.

http://www.who.int/maternal_child_adolescent/child/nurturing-care-framework/en/

Every Newborn Action Plan (2014)

The Plan presents evidence-based means to prevent newborn deaths and stillbirths and a plan up to

2020 with global and national milestones and strategic actions that build on commitments made to

end preventable newborn deaths and stillbirths.

http://www.who.int/maternal_child_adolescent/newborns/every-newborn/en/

BreatheLife (2016)

A joint campaign led by the WHO, UN Environment and the Climate and Clean Air Coalition to

mobilize cities and individuals to protect our health and the planet from the effects of air pollution.


http://www.who.int/mediacentre/news/releases/2016/wha69-27-may-2016/en/

http://www.who.int/sustainable-development/news-events/wha69-roadmap-ap/en/

http://www.un.org/sustainabledevelopment/sustainable-development-goals/

http://www.who.int/life-course/partners/global-strategy/en/


95

The Climate and Clean Air Coalition (2012)

The aim of an initiative comprising over 50 governments and 51 civil society and international

organizations is to reduce emissions of short-lived climate pollutants. The Coalition catalyses action

for rapid introduction of proven technologies and policies to reduce emissions of these pollutants in

all sectors.

http://www.ccacoalition.org/en

The Global Alliance for Clean Cookstoves (2011)

An initiative launched by the United Nations Foundation to deliver clean cooking solutions to 100

million households by 2020.

http://cleancookstoves.org

Sustainable Energy for All (2011)

A partnership co-chaired by the Secretary-General of the United Nations and The World Bank for

achieving universal access to modern energy sources for cooking, heating, lighting and other uses. Its

“global tracking framework” is an approach for monitoring various tiers of access to energy,

“diagnose” situations and form the basis of interventions to move up those tiers towards cleaner,

healthier, more reliable energy sources. The WHO global household energy database is used to

measure progress toward achieving SDG 7.

https://www.seforall.org

UN-Energy (2004)

The mechanism used for inter-agency collaboration in the field of energy to ensure coherence in the

multidisciplinary response of the United Nations system to the outcomes of the World Summit on

Sustainable Development and to support countries in their transition to sustainable energy. http://www.un-energy.org

Energising Development (EnDev)

A partnership to promote access to energy, which is currently financed by six donor countries:

Germany, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. EnDev promotes

sustainable access to modern energy sources that meet the needs of the poor in 25 countries in Africa,

Asia and Latin America.

http://www.who.int/airpollution/household/policy-governance/collaborations/en/

Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (2013)

A plan prepared by WHO and UNICEF that proposes a cohesive approach to end, by 2025,

preventable childhood deaths due to pneumonia and diarrhoea by bringing together critical services

and interventions to promote good health practices, appropriate treatment and universal vaccination

coverage.

http://www.who.int/maternal_child_adolescent/documents/global_action_plan_pneumonia_diarrhoea

/en/

Unmask My City

This global campaign involves local health partners and their communities in promoting practical

solutions and creating tangible policy changes for a clear downward trend in urban air pollution by

2030. It is an initiative of the Global Climate and Health Alliance and its partners Health Care

Without Harm, the Health and Environment Alliance, the US Climate and Health Alliance and the

United Kingdom Health Alliance for Climate Change.

http://unmaskmycity.org/about/

The World Health Assembly resolution on climate change and health (WHA61.19) (2008) and the

Paris Agreement of the United Nations Framework Convention on Climate Change (2015)

These global agreements provide a framework to protect health from risks associated with climate

change and to ensure that actions to mitigate climate change also protect and improve people’s

http://www.ccacoalition.org/en

http://cleancookstoves.org/

https://www.seforall.org/

http://www.un-energy.org/

http://www.who.int/airpollution/household/policy-governance/collaborations/en/

http://www.who.int/maternal_child_adolescent/documents/global_action_plan_pneumonia_diarrhoea/en/

http://www.who.int/maternal_child_adolescent/documents/global_action_plan_pneumonia_diarrhoea/en/

http://unmaskmycity.org/about/

96

health. The Paris Agreement explicitly states that action to address climate change should include,

respect and promote the right to health.

http://www.who.int/globalchange/A61_R19_en.pdf

https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

https://unfccc.int/news/climate-change-agreement-is-public-health-agreement

http://www.who.int/globalchange/A61_R19_en.pdf

https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

https://unfccc.int/news/climate-change-agreement-is-public-health-agreement

97

Annex 2. Additional tables and figures

Table 4. Exposure of children to ambient PM2.5 and burden of disease, by country, 2016

Country Sex

PM2.5_expos

ure_total

(µg/m3)

PM2.5_

exposur

e_rural

(µg/m3)

PM2.5_expo

sure_urban

(µg/m3)

No. of DALYs

(< 5 years)

DALYs rate per 100 000

(< 5 years)

No. of

DALYs

(5–14

years)

DALYs rate

per 100 000

(5–14 years)

No. of

deaths (< 5

years)

Death rate

per 100

000 (< 5

years)

No. of

deaths (5–

14 years)

Death rate per 100 000

(5–14 years)

Afghanistan Both 53.2 48.7 59.9 386 056.4 7 377.6 27 973.4 280.7 4 245.8 81.1 332.4 3.3

F

201 830.5 7 933.1 16 339.0 336.4 2 221.6 87.3 194.4 4.0

M

184 225.9 6 851.9 11 634.3 227.6 2 024.2 75.3 138.0 2.7

Albania Both 17.9 16.9 18.2 726.6 409.6 119.2 34.9 7.8 4.4 1.4 0.4

F

344.5 402.0 84.7 51.8 3.7 4.3 1.0 0.6

M

382.0 416.7 34.5 19.4 4.1 4.5 0.4 0.2

Algeria Both 35.2 37.6 34.5 77 126.5 1 641.3 3 469.9 49.0 844.5 18.0 40.2 0.6

F

45 400.9 1 974.4 1 810.1 52.2 497.4 21.6 21.0 0.6

M

31 725.6 1 322.1 1 659.8 46.0 347.1 14.5 19.2 0.5

Andorra Both 9.9 9.1 11.5 NA NA NA NA NA NA NA NA

F

NA NA NA NA NA NA NA NA

M


Angola Both 27.9 27.7 28.4 353 080.3 6 690.8 17 186.6 208.2 3 884.9 73.6 205.0 2.5

F

185 045.9 7 043.5 8 587.9 206.1 2 037.8 77.6 102.3 2.5

M

168 034.3 6 341.2 8 598.7 210.4 1 847.1 69.7 102.6 2.5

Antigua and

Barbuda

Both 17.9 14.9 18.0 4.8 59.5 3.0 18.2 0.1 0.6 0.0 0.2

F

1.9 46.3 0.1 1.2 0.0 0.5 0.0 0.0

M

2.9 72.5 2.9 35.1 0.0 0.8 0.0 0.4

Argentina Both 11.8 12.3 11.7 5 940.4 159.0 619.3 8.5 64.6 1.7 7.2 0.1

F

2 702.2 147.3 321.3 9.0 29.4 1.6 3.8 0.1

98

M

3 238.2 170.4 298.0 8.1 35.2 1.9 3.4 0.1

Armenia Both 30.5 23.7 32.9 1 191.6 589.3 155.0 40.8 13.0 6.4 1.8 0.5

F

551.1 581.7 70.9 40.0 6.0 6.3 0.8 0.5

M

640.5 596.0 84.2 41.4 7.0 6.5 1.0 0.5

Australia Both 7.2 6.1 7.3 228.6 14.7 47.2 1.6 2.4 0.2 0.5 0.0

F

100.6 13.3 33.4 2.3 1.1 0.1 0.4 0.0

M

128.0 16.1 13.7 0.9 1.3 0.2 0.1 0.0

Austria Both 12.4 10.9 13.1 15.4 3.8 36.0 4.4 0.2 0.0 0.4 0.1

F

7.4 3.7 35.3 8.9 0.1 0.0 0.4 0.1

M

8.1 3.8 0.7 0.2 0.1 0.0 0.0 0.0

Azerbaijan Both 18.2 17.8 18.5 9 954.8 1 117.9 911.8 66.9 108.5 12.2 10.7 0.8

F

5 118.5 1 235.5 410.6 65.0 55.8 13.5 4.8 0.8

M

4 836.3 1 015.6 501.2 68.6 52.7 11.1 5.8 0.8

Bahamas Both 17.6 15.6 19.0 124.4 451.8 7.3 13.8 1.4 4.9 0.1 0.2

F

59.8 446.5 3.1 11.9 0.6 4.8 0.0 0.1

M

64.6 456.9 4.2 15.6 0.7 5.0 0.0 0.2

Bahrain Both 69.0 70.7 69.0 110.7 103.6 79.8 43.7 1.2 1.1 0.9 0.5

F

56.2 108.2 44.2 49.3 0.6 1.1 0.5 0.6

M

54.5 99.4 35.6 38.3 0.6 1.0 0.4 0.4

Bangladesh Both 58.3 52.9 58.6 533 035.9 3 498.6 22 646.5 71.1 5 832.3 38.3 266.6 0.8

F

239 240.0 3 210.1 13 376.9 85.8 2 618.1 35.1 158.3 1.0

M

293 795.9 3 775.0 9 269.6 57.0 3 214.2 41.3 108.3 0.7

Barbados Both 22.2 20.3 22.4 18.9 109.0 9.9 26.5 0.2 1.1 0.1 0.3

F

9.0 105.0 1.6 8.9 0.1 1.1 0.0 0.1

M

9.9 113.0 8.3 43.1 0.1 1.2 0.1 0.5

Belarus Both 18.1 16.4 19.3 417.4 72.0 70.9 7.2 4.4 0.8 0.6 0.1

F

184.2 65.5 34.0 7.1 1.9 0.7 0.3 0.1

M

233.2 78.2 36.9 7.3 2.5 0.8 0.3 0.1

99

Belgium Both 12.9 9.4 13.0 147.6 23.0 22.0 1.7 1.6 0.2 0.2 0.0

F

62.8 20.0 13.3 2.1 0.7 0.2 0.1 0.0

M

84.8 25.7 8.6 1.3 0.9 0.3 0.1 0.0

Belize Both 21.2 21.2 20.9 145.7 361.7 17.7 23.0 1.6 3.9 0.2 0.3

F

71.8 360.2 7.1 18.8 0.8 3.9 0.1 0.2

M

73.9 363.1 10.6 27.1 0.8 3.9 0.1 0.3

Benin Both 33.1 41.3 30.4 138 222.6 7 785.2 14 098.4 488.7 1 520.1 85.6 168.6 5.8

F

72 869.8 8 336.8 7 239.4 509.0 801.8 91.7 86.5 6.1

M

65 352.8 7 250.3 6 859.1 468.9 718.2 79.7 82.1 5.6

Bhutan Both 35.3 35.3 35.4 1 704.3 2 441.1 169.0 116.5 18.6 26.7 2.0 1.4

F

766.9 2 234.3 92.6 129.7 8.4 24.4 1.1 1.6

M

937.4 2 641.1 76.4 103.7 10.2 28.9 0.9 1.2

Bolivia

(Plurinational

State of)

Both 20.2 18.4 23.3 20 868.2 1 755.8 2 703.8 117.6 228.6 19.2 32.4 1.4

F

9 788.9 1 681.8 1 287.7 114.0 107.2 18.4 15.5 1.4

M

11 079.3 1 826.9 1 416.1 121.1 121.3 20.0 17.0 1.5

Bosnia and

Herzegovina

Both 27.3 24.3 29.7 204.5 130.5 21.9 6.4 2.2 1.4 0.2 0.1

F

95.5 125.7 8.9 5.4 1.0 1.3 0.1 0.0

M

109.0 135.0 12.9 7.3 1.1 1.4 0.1 0.1

Botswana Both 21.2 21.3 20.9 3 767.7 1 453.5 389.3 86.0 41.3 15.9 4.6 1.0

F

1 818.0 1 417.1 177.2 78.9 19.9 15.5 2.1 0.9

M

1 949.7 1 489.2 212.1 93.1 21.4 16.3 2.5 1.1

Brazil Both 11.5 9.5 11.8 40 961.5 274.6 3 745.2 12.1 445.3 3.0 43.0 0.1

F

18 929.2 259.8 1 786.6 11.7 205.7 2.8 20.4 0.1

M

22 032.3 288.7 1 958.6 12.4 239.7 3.1 22.6 0.1

Brunei

Darussalam

Both 5.8 5.8 5.8 6.8 19.7 3.8 5.9 0.1 0.2 0.0 0.1

F

3.1 18.4 2.5 8.1 0.0 0.2 0.0 0.1

100

M

3.7 20.9 1.3 3.8 0.0 0.2 0.0 0.0

Bulgaria Both 18.8 17.7 20.8 1 139.7 351.5 218.6 32.1 12.4 3.8 2.5 0.4

F

509.6 323.6 101.2 30.6 5.5 3.5 1.2 0.4

M

630.1 377.9 117.4 33.6 6.8 4.1 1.4 0.4

Burkina Faso Both 36.8 37.2 36.3 184 238.3 5 720.5 23 174.6 442.1 2 025.8 62.9 277.4 5.3

F

87 392.1 5 524.4 12 790.2 496.9 961.5 60.8 153.0 5.9

M

96 846.2 5 909.9 10 384.3 389.2 1 064.3 65.0 124.4 4.7

Burundi Both 35.6 35.2 35.6 113 273.1 5 957.5 19 974.4 708.0 1 244.4 65.4 239.8 8.5

F

54 101.3 5 728.0 10 650.6 752.5 594.6 62.9 127.8 9.0

M

59 171.8 6 184.1 9 323.8 663.2 649.8 67.9 112.0 8.0

Cabo Verde Both 32.0 33.6 31.6 687.7 1 258.9 32.9 29.7 7.5 13.8 0.4 0.4

F

310.7 1 151.3 15.8 28.7 3.4 12.6 0.2 0.3

M

377.0 1 363.9 17.2 30.7 4.1 14.9 0.2 0.4

Cambodia Both 24.0 21.5 24.9 35 159.4 1 996.2 3 230.5 101.4 383.9 21.8 38.0 1.2

F

15 942.8 1 839.9 1 399.9 89.5 174.2 20.1 16.4 1.1

M

19 216.6 2 147.7 1 830.6 112.8 209.7 23.4 21.6 1.3

Cameroon Both 65.3 65.1 65.4 344 937.9 9 067.2 64 364.4 1 029.5 3 790.9 99.7 770.3 12.3

F

156 001.3 8 284.3 30 891.9 995.8 1 715.1 91.1 369.7 11.9

M

188 936.6 9 834.5 33 472.5 1 062.6 2 075.8 108.0 400.6 12.7

Canada Both 6.5 5.3 6.7 157.4 8.2 28.9 0.7 1.7 0.1 0.3 0.0

F

72.7 7.7 16.9 0.9 0.8 0.1 0.2 0.0

M

84.7 8.6 12.1 0.6 0.9 0.1 0.1 0.0

Central

African

Republic

Both 49.5 49.0 51.2 94 519.2 12 939.8 4 449.3 350.8 1 037.9 142.1 53.2 4.2

F

48 299.8 13 244.6 2 338.6 366.3 530.8 145.5 27.9 4.4

M

46 219.4 12 635.9 2 110.7 335.1 507.1 138.6 25.2 4.0

Chad Both 53.0 53.6 50.8 547 683.0 20 540.3 28 618.3 684.4 6 039.4 226.5 343.8 8.2

F

263 619.5 19 947.5 15 518.3 748.0 2 908.3 220.1 186.5 9.0

101

M

284 063.5 21 122.8 13 100.0 621.7 3 131.1 232.8 157.3 7.5

Chile Both 21.0 17.8 23.1 1 071.5 90.5 83.5 3.3 11.6 1.0 0.9 0.0

F

480.8 82.8 40.7 3.3 5.2 0.9 0.4 0.0

M

590.8 97.9 42.8 3.4 6.4 1.1 0.4 0.0

China Both 49.2 35.7 51.0 611 083.6 709.1 46 469.9 28.5 6 645.8 7.7 515.0 0.3

F

270 327.0 675.7 26 131.2 34.6 2 930.2 7.3 279.1 0.4

M

340 756.6 738.0 20 338.7 23.2 3 715.6 8.0 235.9 0.3

Colombia Both 15.2 13.4 17.2 15 240.6 410.6 1 558.1 19.7 166.5 4.5 18.6 0.2

F

7 085.8 390.2 674.2 17.4 77.4 4.3 8.0 0.2

M

8 154.8 430.1 884.0 21.9 89.1 4.7 10.6 0.3

Comoros Both 18.6 18.4 18.6 5 320.3 4 465.4 294.4 148.0 58.4 49.0 3.5 1.8

F

2 609.0 4 464.0 142.8 146.1 28.7 49.1 1.7 1.7

M

2 711.3 4 466.7 151.6 149.8 29.8 49.0 1.8 1.8

Congo Both 38.7 41.4 36.4 33 752.7 4 094.9 2 446.2 181.4 370.5 45.0 29.4 2.2

F

16 543.0 4 052.4 1 384.0 206.4 181.8 44.5 16.6 2.5

M

17 209.8 4 136.6 1 062.2 156.7 188.7 45.4 12.7 1.9

Cook Islands Both 12.0 12.0 NA NA NA NA NA NA NA NA NA

F


M


Costa Rica Both 15.9 12.6 16.7 403.1 116.6 51.7 7.2 4.3 1.3 0.5 0.1

F

188.1 111.5 28.1 8.0 2.0 1.2 0.3 0.1

M

215.0 121.5 23.6 6.4 2.3 1.3 0.3 0.1

Côte d'Ivoire Both 23.7 23.5 23.9 248 406.3 6 434.4 36 550.7 586.9 2 728.3 70.7 439.2 7.1

F

110 022.3 5 738.8 20 246.4 651.2 1 208.9 63.1 243.3 7.8

M

138 384.0 7 120.6 16 304.3 522.8 1 519.4 78.2 195.9 6.3

Croatia Both 17.0 15.8 17.6 106.9 54.5 15.1 3.6 1.1 0.6 0.2 0.0

F

44.0 46.1 1.0 0.5 0.5 0.5 0.0 0.0

M

63.0 62.4 14.1 6.5 0.7 0.7 0.2 0.1

102

Cuba Both 18.4 15.8 21.6 1 241.4 195.1 93.1 7.6 13.5 2.1 1.0 0.1

F

571.2 184.5 58.2 9.8 6.2 2.0 0.7 0.1

M

670.2 205.1 34.9 5.6 7.3 2.2 0.3 0.1

Cyprus Both 16.8 15.9 17.1 5.7 8.7 0.2 0.1 0.1 0.1 0.0 0.0

F

2.7 8.6 0.1 0.1 0.0 0.1 0.0 0.0

M

3.0 8.8 0.1 0.1 0.0 0.1 0.0 0.0

Czechia Both 15.1 13.6 15.6 296.9 55.6 89.1 8.2 3.2 0.6 1.0 0.1

F

122.2 47.1 32.9 6.2 1.3 0.5 0.3 0.1

M

174.7 63.7 56.2 10.1 1.9 0.7 0.6 0.1

Democratic

People's

Republic of

Korea

Both 30.4 29.0 31.0 23 721.3 1 374.0 1 592.9 44.6 258.9 15.0 18.3 0.5

F

10 458.3 1 240.5 882.3 50.5 114.0 13.5 10.1 0.6

M

13 263.0 1 501.5 710.6 39.0 144.9 16.4 8.2 0.5

Democratic

Republic of

the Congo

Both 37.6 37.8 37.4 1 175 790.2 8 112.2 90 407.6 411.0 12 890.7 88.9 1 088.9 5.0

F

585 953.1 8 166.0 49 954.8 457.5 6 426.4 89.6 601.4 5.5

M

589 837.2 8 059.6 40 452.8 365.2 6 464.4 88.3 487.5 4.4

Denmark Both 10.1 9.5 10.3 35.6 12.5 3.9 0.6 0.4 0.1 0.0 0.0

F

16.7 12.0 3.3 1.0 0.2 0.1 0.0 0.0

M

19.0 13.0 0.6 0.2 0.2 0.1 0.0 0.0

Djibouti Both 40.4 40.1 41.0 4 942.5 4 832.2 1 142.5 585.3 54.1 52.9 13.8 7.1

F

2 281.4 4 524.2 556.6 577.3 25.0 49.5 6.7 7.0

M

2 661.1 5 131.7 585.9 593.1 29.1 56.1 7.1 7.2

Dominica Both 18.2 16.9 18.8 NA NA NA NA NA NA NA NA

F


M


Dominican

Republic

Both 12.9 11.3 13.3 8 736.8 824.6 466.1 22.2 95.7 9.0 5.4 0.3

103

F

3 481.5 671.0 221.4 21.5 38.1 7.3 2.6 0.2

M

5 255.4 972.0 244.7 23.0 57.6 10.7 2.8 0.3

Ecuador Both 14.9 14.1 15.5 10 798.2 670.2 866.4 28.0 118.1 7.3 10.3 0.3

F

4 993.3 634.5 408.9 27.0 54.7 6.9 4.9 0.3

M

5 804.9 704.3 457.5 29.0 63.5 7.7 5.5 0.3

Egypt Both 79.3 69.4 79.6 222 583.9 1 728.7 56 687.9 296.3 2 431.9 18.9 675.5 3.5

F

97 331.7 1 560.2 25 258.7 272.1 1 063.8 17.1 300.1 3.2

M

125 252.2 1 887.0 31 429.2 319.0 1 368.1 20.6 375.4 3.8

El Salvador Both 23.4 20.7 23.8 3 664.6 635.7 516.9 43.6 40.0 6.9 6.3 0.5

F

1 427.1 506.9 186.3 32.1 15.5 5.5 2.2 0.4

M

2 237.5 758.5 330.6 54.6 24.4 8.3 4.1 0.7

Equatorial

Guinea

Both 45.9 45.5 49.1 15 735.2 8 662.9 1 469.4 535.1 172.6 95.0 17.6 6.4

F

6 747.9 7 527.1 811.9 599.2 74.0 82.6 9.7 7.2

M

8 987.3 9 769.8 657.5 472.6 98.5 107.1 7.9 5.7

Eritrea Both 42.4 42.9 41.1 38 460.1 5 171.2 4 830.2 360.5 422.3 56.8 57.6 4.3

F

17 784.4 4 884.5 2 226.2 338.9 195.3 53.6 26.5 4.0

M

20 675.7 5 446.1 2 604.0 381.2 227.0 59.8 31.1 4.6

Estonia Both 6.7 6.2 7.0 9.5 14.0 5.9 4.1 0.1 0.1 0.1 0.0

F

4.5 13.5 2.3 3.3 0.0 0.1 0.0 0.0

M

5.0 14.6 3.6 4.9 0.1 0.1 0.0 0.0

eSwatini Both 16.3 16.4 16.2 5 927.3 3 300.2 582.3 180.5 64.9 36.1 6.9 2.2

F

2 764.4 3 098.5 302.6 188.2 30.3 33.9 3.6 2.2

M

3 162.9 3 499.1 279.7 172.8 34.6 38.3 3.3 2.1

Ethiopia Both 34.4 34.9 34.0 703 519.9 4 635.4 100 742.9 374.4 7 733.5 51.0 1 209.0 4.5

F

313 661.3 4 195.2 47 635.0 357.8 3 447.9 46.1 571.2 4.3

M

389 858.5 5 062.7 53 107.9 390.5 4 285.6 55.7 637.7 4.7

Fiji Both 10.2 9.7 10.5 440.5 508.7 57.6 33.8 4.8 5.5 0.7 0.4

F

213.3 506.5 29.5 35.8 2.3 5.5 0.3 0.4

104

M

227.2 510.8 28.1 31.9 2.5 5.6 0.3 0.4

Finland Both 5.9 5.5 6.5 11.4 3.9 0.4 0.1 0.1 0.0 0.0 0.0

F

5.4 3.7 0.2 0.1 0.1 0.0 0.0 0.0

M

6.0 4.0 0.3 0.1 0.1 0.0 0.0 0.0

France Both 11.6 9.9 12.4 354.4 9.2 95.0 1.2 3.6 0.1 0.7 0.0

F

169.4 9.0 44.1 1.1 1.7 0.1 0.3 0.0

M

185.0 9.4 50.8 1.3 1.9 0.1 0.4 0.0

Gabon Both 38.5 38.8 37.8 9 337.7 3 412.7 1 320.2 302.5 102.4 37.4 16.2 3.7

F

4 241.4 3 134.8 635.5 293.9 46.6 34.4 7.8 3.6

M

5 096.3 3 684.6 684.7 311.1 55.9 40.4 8.4 3.8

Gambia Both 32.2 31.7 32.3 16 104.3 4 467.4 1 452.4 255.8 177.3 49.2 17.3 3.1

F

7 641.2 4 282.0 840.0 298.5 84.2 47.2 10.0 3.6

M

8 463.0 4 649.3 612.4 213.9 93.1 51.2 7.3 2.6

Georgia Both 21.2 17.9 24.0 662.8 244.4 235.7 49.9 7.2 2.7 2.8 0.6

F

330.1 252.8 86.9 38.9 3.6 2.7 1.0 0.5

M

332.7 236.6 148.7 59.6 3.6 2.6 1.8 0.7

Germany Both 11.7 10.5 11.9 487.6 13.7 137.5 1.9 5.2 0.1 1.5 0.0

F

221.7 12.8 51.1 1.5 2.4 0.1 0.5 0.0

M

265.9 14.6 86.4 2.4 2.8 0.2 1.0 0.0

Ghana Both 31.9 34.0 31.1 132 424.2 3 241.5 13 607.8 199.4 1 454.6 35.6 163.1 2.4

F

58 899.7 2 944.6 6 977.2 209.1 646.9 32.3 83.6 2.5

M

73 524.5 3 526.3 6 630.6 190.1 807.7 38.7 79.5 2.3

Greece Both 15.7 13.5 16.4 209.0 44.1 32.2 2.9 2.3 0.5 0.4 0.0

F

95.2 41.5 14.6 2.7 1.0 0.5 0.2 0.0

M

113.8 46.6 17.6 3.0 1.2 0.5 0.2 0.0

Grenada Both 21.6 20.4 21.8 64.8 656.6 2.1 11.6 0.7 7.1 0.0 0.1

F

30.5 633.5 0.1 1.7 0.3 6.9 0.0 0.0

M

34.3 678.5 2.0 21.1 0.4 7.4 0.0 0.2

105

Guatemala Both 23.6 21.0 24.2 33 930.6 1 677.5 2 561.5 65.8 371.2 18.4 30.6 0.8

F

15 724.5 1 589.9 1 272.7 66.8 172.0 17.4 15.2 0.8

M

18 206.1 1 761.4 1 288.8 64.9 199.2 19.3 15.4 0.8

Guinea Both 22.4 22.6 22.2 114 271.7 5 762.9 12 485.4 380.5 1 255.6 63.3 149.5 4.6

F

54 110.4 5 492.7 7 599.1 466.5 594.8 60.4 90.9 5.6

M

60 161.3 6 029.7 4 886.2 295.8 660.8 66.2 58.6 3.5

Guinea-Bissau Both 27.1 27.6 26.5 20 053.3 6 895.0 1 125.1 242.1 221.1 76.0 13.5 2.9

F

9 528.5 6 581.2 587.9 253.1 105.1 72.6 7.0 3.0

M

10 524.9 7 206.1 537.3 231.1 116.1 79.5 6.4 2.8

Guyana Both 20.5 17.4 21.6 702.5 919.3 122.6 81.2 7.7 10.0 1.5 1.0

F

325.6 874.3 53.1 72.2 3.6 9.6 0.6 0.9

M

376.9 962.0 69.5 89.6 4.1 10.5 0.8 1.1

Haiti Both 14.6 13.3 14.7 49 247.9 3 992.8 3 714.0 155.8 540.6 43.8 44.4 1.9

F

22 195.9 3 670.9 1 570.6 133.9 243.7 40.3 18.8 1.6

M

27 052.0 4 302.3 2 143.4 177.1 296.9 47.2 25.6 2.1

Honduras Both 20.1 18.9 21.5 6 832.2 718.2 276.6 13.9 74.7 7.8 3.2 0.2

F

3 055.7 655.5 78.0 8.0 33.4 7.2 0.9 0.1

M

3 776.4 778.5 198.6 19.6 41.3 8.5 2.4 0.2

Hungary Both 15.6 14.4 16.3 303.6 69.6 12.8 1.3 3.2 0.7 0.0 0.0

F

127.2 60.2 6.1 1.3 1.3 0.6 0.0 0.0

M

176.4 78.6 6.7 1.4 1.9 0.8 0.0 0.0

Iceland Both 5.9 6.0 5.9 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

F

0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

M

0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

India Both 65.2 55.9 68.0 5 560 430.5 4 633.8 368 945.7 145.6 60 987.2 50.8 4 360.5 1.7

F

2 996 229.5 5 270.4 205 376.5 171.5 32 889.5 57.9 2 441.7 2.0

M

2 564 201.0 4 060.6 163 569.2 122.4 28 097.7 44.5 1 918.9 1.4

Indonesia Both 15.6 13.2 16.4 265 856.9 1 071.1 16 089.2 34.0 2 902.5 11.7 187.5 0.4

106

F

130 505.2 1 076.1 7 446.9 32.2 1 425.5 11.8 86.4 0.4

M

135 351.7 1 066.2 8 642.3 35.6 1 477.0 11.6 101.1 0.4

Iran (Islamic

Republic of)

Both 35.1 35.6 34.4 64 015.8 938.3 2 711.3 22.3 698.5 10.2 29.9 0.2

F

33 860.4 1 014.1 1 344.0 22.5 370.0 11.1 14.8 0.2

M

30 155.4 865.6 1 367.3 22.0 328.5 9.4 15.1 0.2

Iraq Both 57.7 54.8 60.1 181 782.9 3 168.3 3 681.5 39.4 1 988.9 34.7 41.6 0.4

F

59 381.7 2 129.3 1 993.5 43.9 650.6 23.3 22.7 0.5

M

122 401.2 4 150.9 1 688.0 35.1 1 338.3 45.4 18.9 0.4

Ireland Both 8.3 7.6 8.7 31.3 9.1 3.4 0.5 0.3 0.1 0.0 0.0

F

13.5 8.1 2.7 0.8 0.1 0.1 0.0 0.0

M

17.8 10.1 0.7 0.2 0.2 0.1 0.0 0.0

Israel Both 19.5 21.1 19.4 148.5 17.7 58.7 4.1 1.6 0.2 0.7 0.0

F

72.5 17.7 25.5 3.6 0.8 0.2 0.3 0.0

M

76.0 17.6 33.3 4.5 0.8 0.2 0.4 0.1

Italy Both 15.3 11.1 15.7 279.8 11.3 63.8 1.1 3.0 0.1 0.7 0.0

F

125.5 10.4 43.0 1.6 1.3 0.1 0.5 0.0

M

154.2 12.1 20.8 0.7 1.6 0.1 0.2 0.0

Jamaica Both 13.3 12.5 13.6 316.6 154.4 48.6 10.6 3.4 1.7 0.5 0.1

F

144.1 144.9 24.0 10.7 1.5 1.6 0.3 0.1

M

172.6 163.4 24.6 10.6 1.9 1.8 0.3 0.1

Japan Both 11.4 9.3 11.8 1 882.6 35.2 527.3 4.7 20.2 0.4 5.4 0.0

F

885.3 34.1 216.7 4.0 9.5 0.4 2.2 0.0

M

997.2 36.3 310.6 5.4 10.7 0.4 3.2 0.1

Jordan Both 32.1 38.0 31.7 8 581.1 699.3 672.2 31.2 93.5 7.6 7.8 0.4

F

4 112.6 684.8 388.5 36.5 44.8 7.5 4.5 0.4

M

4 468.5 713.2 283.7 26.0 48.6 7.8 3.2 0.3

Kazakhstan Both 11.3 10.4 14.5 5 701.0 285.5 390.6 13.3 62.1 3.1 4.4 0.2

F

2 567.9 264.5 187.5 13.1 28.0 2.9 2.1 0.1

107

M

3 133.1 305.3 203.1 13.5 34.1 3.3 2.3 0.2

Kenya Both 25.9 26.1 25.8 215 951.5 3 074.8 13 977.7 109.3 2 370.7 33.8 167.5 1.3

F

104 676.1 3 011.9 5 498.3 86.6 1 149.5 33.1 65.7 1.0

M

111 275.4 3 136.3 8 479.4 131.6 1 221.2 34.4 101.8 1.6

Kiribati Both 10.5 10.4 10.9 283.7 1 958.7 16.6 65.7 3.1 21.5 0.2 0.8

F

136.5 1 932.1 4.8 38.6 1.5 21.2 0.1 0.4

M

147.2 1 984.1 11.9 91.7 1.6 21.8 0.1 1.1

Kuwait Both 57.2 52.5 58.9 1 046.9 331.2 219.3 41.1 11.4 3.6 2.5 0.5

F

517.5 334.8 99.8 39.0 5.6 3.6 1.1 0.4

M

529.4 327.7 119.5 43.0 5.7 3.6 1.4 0.5

Kyrgyzstan Both 18.1 18.7 17.4 10 768.6 1 416.0 448.8 40.2 117.7 15.5 5.2 0.5

F

4 973.2 1 344.2 196.4 35.9 54.3 14.7 2.2 0.4

M

5 795.4 1 484.0 252.4 44.3 63.3 16.2 2.9 0.5

Lao People's

Democratic

Republic

Both 24.5 23.7 25.5 33 457.6 4 370.1 2 398.2 161.7 366.7 47.9 28.4 1.9

F

16 173.4 4 314.4 1 085.7 149.4 177.4 47.3 12.8 1.8

M

17 284.2 4 423.4 1 312.5 173.6 189.3 48.4 15.5 2.1

Latvia Both 12.7 10.8 14.4 66.6 68.8 3.6 1.8 0.7 0.7 0.0 0.0

F

31.8 67.7 1.7 1.7 0.3 0.7 0.0 0.0

M

34.8 69.8 1.9 1.8 0.4 0.7 0.0 0.0

Lebanon Both 30.7 29.7 30.7 937.8 194.1 69.3 7.4 10.1 2.1 0.6 0.1

F

516.4 217.0 37.1 7.9 5.6 2.3 0.3 0.1

M

421.4 171.8 32.1 7.0 4.5 1.9 0.3 0.1

Lesotho Both 27.8 27.3 28.1 17 410.3 6 089.3 1 225.1 246.4 190.7 66.7 14.6 2.9

F

8 789.9 6 191.7 681.4 275.5 96.3 67.8 8.2 3.3

M

8 620.5 5 988.2 543.7 217.7 94.4 65.6 6.5 2.6

Liberia Both 17.2 17.4 17.0 24 483.1 3 421.9 1 996.1 162.6 268.2 37.5 24.0 2.0

F

12 191.9 3 482.8 1 088.0 181.1 133.6 38.2 13.1 2.2

108

M

12 291.1 3 363.5 908.1 144.9 134.6 36.8 10.9 1.7

Libya Both 44.2 44.9 41.7 4 146.2 661.8 735.2 63.3 45.3 7.2 8.4 0.7

F

2 155.4 705.9 354.6 62.6 23.5 7.7 4.0 0.7

M

1 990.8 619.8 380.6 64.0 21.7 6.8 4.4 0.7

Lithuania Both 11.5 10.9 12.3 66.7 43.9 3.9 1.4 0.7 0.5 0.0 0.0

F

34.1 46.0 1.9 1.4 0.4 0.5 0.0 0.0

M

32.6 41.8 2.0 1.4 0.3 0.4 0.0 0.0

Luxembourg Both 10.2 8.8 10.4 1.0 3.1 0.1 0.1 0.0 0.0 0.0 0.0

F

0.4 2.8 0.0 0.1 0.0 0.0 0.0 0.0

M

0.5 3.3 0.0 0.1 0.0 0.0 0.0 0.0

Madagascar Both 21.4 20.7 22.5 111 123.1 2 948.7 14 298.0 219.6 1 218.9 32.3 171.2 2.6

F

54 845.3 2 946.9 6 740.7 208.3 601.8 32.3 80.5 2.5

M

56 277.8 2 950.4 7 557.3 230.8 617.0 32.3 90.6 2.8

Malawi Both 22.1 23.3 21.9 78 573.0 2 701.7 4 617.6 90.5 861.8 29.6 55.1 1.1

F

36 320.7 2 526.5 2 056.0 81.1 398.3 27.7 24.4 1.0

M

42 252.3 2 873.0 2 561.5 99.9 463.4 31.5 30.7 1.2

Malaysia Both 16.0 11.6 17.3 3 312.3 126.8 1,170.5 23.1 35.8 1.4 13.8 0.3

F

1 425.4 113.1 509.3 20.7 15.4 1.2 6.0 0.2

M

1 886.9 139.6 661.2 25.4 20.4 1.5 7.9 0.3

Maldives Both 7.6 7.6 7.7 28.2 72.3 4.2 6.8 0.3 0.8 0.0 0.1

F

15.2 81.3 2.2 7.5 0.2 0.9 0.0 0.1

M

13.0 64.0 1.9 6.2 0.1 0.7 0.0 0.1

Mali Both 31.2 32.7 29.0 248 818.1 7 467.3 16 723.2 317.0 2 737.2 82.1 199.4 3.8

F

126 586.9 7 736.3 9 540.6 367.5 1 393.1 85.1 113.8 4.4

M

122 231.3 7 207.7 7 182.6 268.0 1 344.2 79.3 85.6 3.2

Malta Both 14.0 10.1 14.0 9.1 42.2 0.1 0.1 0.1 0.5 0.0 0.0

F

4.6 43.9 0.0 0.1 0.1 0.5 0.0 0.0

M

4.5 40.5 0.0 0.1 0.0 0.4 0.0 0.0

109

Marshall

Islands

Both 9.4 9.4 NA NA NA NA NA NA NA NA NA

F


M


Mauritania Both 40.8 40.0 41.7 46 817.5 7 146.5 2 088.2 195.2 515.1 78.6 24.8 2.3

F

19 235.3 5 982.5 1 172.9 222.6 211.7 65.8 13.9 2.6

M

27 582.2 8 268.4 915.3 168.6 303.5 91.0 10.9 2.0

Mauritius Both 13.5 14.9 13.5 155.6 228.4 28.0 16.4 1.7 2.5 0.3 0.2

F

65.9 197.4 16.1 19.2 0.7 2.1 0.2 0.2

M

89.7 258.2 11.9 13.7 1.0 2.8 0.1 0.2

Mexico Both 20.1 14.4 20.9 57 023.4 492.4 2 449.4 10.7 623.2 5.4 29.0 0.1

F

26 372.7 466.0 1 107.7 9.9 288.2 5.1 13.0 0.1

M

30 650.7 517.6 1 341.7 11.4 334.9 5.7 16.0 0.1

Micronesia

(Federated

States of)

Both 10.2 10.1 10.5 116.4 999.6 10.7 45.4 1.3 10.9 0.1 0.5

F

53.4 948.9 4.5 39.3 0.6 10.4 0.1 0.5

M

63.0 1 047.0 6.2 51.1 0.7 11.5 0.1 0.6

Monaco Both 12.2 NA 12.2 NA NA NA NA NA NA NA NA

F


M


Mongolia Both 40.4 36.9 49.5 4 467.1 1 215.3 413.2 79.5 48.7 13.2 4.8 0.9

F

1 608.8 886.9 170.3 66.3 17.5 9.7 2.0 0.8

M

2 858.3 1 535.2 242.9 92.5 31.2 16.7 2.8 1.1

Montenegro Both 20.2 20.6 19.3 19.6 54.1 9.4 11.9 0.2 0.5 0.1 0.1

F

8.6 49.1 4.8 12.8 0.1 0.5 0.0 0.1

M

11.0 58.8 4.5 11.1 0.1 0.6 0.0 0.1

Morocco Both 31.0 30.4 31.1 48 582.1 1 384.7 2 072.4 33.3 531.5 15.1 23.9 0.4

F

22 119.4 1 296.0 1 070.0 35.3 242.3 14.2 12.4 0.4

M

26 462.6 1 468.8 1 002.4 31.5 289.2 16.1 11.5 0.4

110

Mozambique Both 19.4 20.1 18.4 174 278.2 3 520.8 15 233.8 189.7 1 908.5 38.6 183.5 2.3

F

85 623.6 3 486.0 7 451.6 186.2 937.8 38.2 89.7 2.2

M

88 654.6 3 555.1 7 782.2 193.3 970.7 38.9 93.8 2.3

Myanmar Both 34.7 34.8 34.6 205 998.5 4 539.6 15 630.7 157.3 2 258.5 49.8 185.8 1.9

F

92 061.5 4 085.8 5 573.1 112.8 1 010.9 44.9 66.1 1.3

M

113 936.9 4 987.1 10 057.6 201.2 1 247.6 54.6 119.7 2.4

Namibia Both 22.6 24.0 21.0 9 710.1 2 821.7 884.3 154.9 106.6 31.0 10.6 1.9

F

4 613.1 2 699.0 395.9 139.0 50.6 29.6 4.7 1.7

M

5 096.9 2 942.8 488.4 170.9 56.0 32.3 5.9 2.1

Nauru Both 12.5 10.0 12.5 NA NA NA NA NA NA NA NA

F


M


Nepal Both 94.3 68.3 99.5 115 948.0 4 206.8 7 958.3 123.9 1 268.4 46.0 93.7 1.5

F

51 581.9 3 863.0 4 026.1 128.5 564.3 42.3 47.5 1.5

M

64 366.1 4 530.0 3 932.2 119.4 704.1 49.6 46.2 1.4

Netherlands Both 12.1 11.0 12.1 106.7 11.9 72.5 3.8 1.2 0.1 0.8 0.0

F

46.4 10.7 50.6 5.4 0.5 0.1 0.6 0.1

M

60.3 13.1 21.9 2.2 0.7 0.1 0.2 0.0

New Zealand Both 5.7 5.2 5.8 82.5 27.1 7.6 1.2 0.9 0.3 0.1 0.0

F

39.2 26.4 3.5 1.2 0.4 0.3 0.0 0.0

M

43.3 27.8 4.1 1.3 0.5 0.3 0.0 0.0

Nicaragua Both 16.9 14.6 19.0 6 290.0 1 053.1 452.9 37.2 68.8 11.5 5.5 0.4

F

2 417.2 827.6 214.0 36.2 26.4 9.0 2.6 0.4

M

3 872.8 1 268.9 238.8 38.1 42.4 13.9 2.9 0.5

Niger Both 70.8 69.7 73.0 545 182.9 12 925.6 77 215.2 1 252.2 6 014.2 142.6 929.3 15.1

F

277 404.3 13 442.9 40 120.8 1 327.8 3 062.3 148.4 482.8 16.0

M

267 778.6 12 430.0 37 094.4 1 179.5 2 951.9 137.0 446.5 14.2

Nigeria Both 48.7 56.5 46.3 4 330 967.5 13 618.7 486 606.3 969.4 47 674.7 149.9 5 810.7 11.6

111

F

1 996 787.6 12 877.7 278 996.4 1 135.2 21 994.6 141.8 3 330.2 13.6

M

2 334 180.0 14 323.9 207 609.9 810.4 25 680.1 157.6 2 480.5 9.7

Niue Both 11.5 11.5 NA NA NA NA NA NA NA NA NA

F


M


Norway Both 7.0 6.4 7.8 15.5 5.1 2.6 0.4 0.2 0.1 0.0 0.0

F

6.4 4.3 0.5 0.2 0.1 0.0 0.0 0.0

M

9.1 5.8 2.1 0.7 0.1 0.1 0.0 0.0

Oman Both 38.2 40.0 36.2 1 380.0 344.9 132.8 23.2 15.0 3.7 1.4 0.2

F

794.5 408.8 68.1 24.0 8.6 4.4 0.7 0.3

M

585.6 284.5 64.7 22.3 6.3 3.1 0.7 0.2

Pakistan Both 55.2 52.0 56.2 1 928 216.0 7 724.4 93 060.9 219.2 21 136.9 84.7 1 110.8 2.6

F

975 687.2 8 129.1 35 489.6 173.8 10 699.8 89.1 422.3 2.1

M

952 528.8 7 349.6 57 571.3 261.4 10 437.1 80.5 688.5 3.1

Palau Both 12.2 12.0 12.4 NA NA NA NA NA NA NA NA

F


M


Panama Both 11.2 9.4 12.0 1 642.7 423.0 148.3 20.4 17.9 4.6 1.8 0.2

F

713.3 375.3 65.1 18.3 7.8 4.1 0.8 0.2

M

929.3 468.7 83.2 22.4 10.1 5.1 1.0 0.3

Papua New

Guinea

Both 10.9 10.8 11.5 18 250.9 1 767.3 1 273.6 67.0 199.6 19.3 15.0 0.8

F

7 956.8 1 595.6 443.8 48.3 87.0 17.4 5.2 0.6

M

10 294.1 1 927.7 829.9 84.6 112.6 21.1 9.8 1.0

Paraguay Both 11.2 10.2 11.7 2 865.3 426.6 300.0 22.6 31.2 4.6 3.3 0.3

F

1 286.8 390.9 144.0 22.1 14.0 4.3 1.6 0.2

M

1 578.5 460.9 156.0 23.1 17.2 5.0 1.8 0.3

Peru Both 24.3 18.4 29.0 17 394.2 573.6 5 134.5 89.2 189.7 6.3 61.1 1.1

F

7 802.9 525.6 2 295.6 81.5 85.1 5.7 27.3 1.0

112

M

9 591.3 619.6 2 838.9 96.7 104.6 6.8 33.8 1.2

Philippines Both 18.4 14.0 18.7 173 752.3 1 506.9 25 630.5 119.2 1 904.2 16.5 308.8 1.4

F

80 151.2 1 431.7 11 809.2 112.9 878.8 15.7 141.9 1.4

M

93 601.1 1 577.9 13 821.4 125.2 1 025.5 17.3 166.9 1.5

Poland Both 20.5 18.0 21.5 1 403.7 77.2 595.6 15.5 15.1 0.8 6.9 0.2

F

625.5 70.7 281.8 15.1 6.7 0.8 3.3 0.2

M

778.2 83.3 313.8 15.9 8.4 0.9 3.6 0.2

Portugal Both 7.9 7.1 8.1 95.0 22.0 9.7 1.0 1.0 0.2 0.1 0.0

F

43.7 21.0 3.5 0.7 0.5 0.2 0.0 0.0

M

51.3 23.0 6.3 1.2 0.6 0.3 0.1 0.0

Qatar Both 90.3 81.3 91.7 385.3 296.5 62.4 27.5 4.2 3.2 0.7 0.3

F

150.5 236.1 24.9 22.6 1.6 2.5 0.3 0.2

M

234.8 354.6 37.5 32.3 2.5 3.8 0.4 0.4

Republic of

Korea

Both 24.6 23.7 24.7 645.2 29.0 163.6 3.5 6.6 0.3 1.2 0.0

F

319.2 29.8 72.6 3.2 3.3 0.3 0.5 0.0

M

326.0 28.3 90.9 3.7 3.3 0.3 0.7 0.0

Republic of

Moldova

Both 16.0 15.2 16.5 1 656.5 760.4 109.3 26.0 18.0 8.3 1.2 0.3

F

709.3 675.2 48.9 24.0 7.7 7.4 0.5 0.3

M

947.2 839.8 60.3 27.9 10.3 9.1 0.7 0.3

Romania Both 14.3 12.7 15.4 6 136.0 649.9 623.5 29.9 66.9 7.1 7.2 0.3

F

2 759.5 600.6 323.4 31.9 30.1 6.5 3.8 0.4

M

3 376.5 696.6 300.0 28.0 36.8 7.6 3.5 0.3

Russian

Federation

Both 13.7 12.2 14.7 11 757.3 123.0 2 015.2 13.1 126.3 1.3 21.4 0.1

F

5 386.8 115.9 1 006.5 13.5 57.8 1.2 10.8 0.1

M

6 370.5 129.6 1 008.7 12.8 68.4 1.4 10.6 0.1

Rwanda Both 40.7 44.0 40.7 44 833.3 2 577.0 8 471.6 274.8 491.3 28.2 101.7 3.3

F

20 259.5 2 338.0 3 931.9 254.3 222.0 25.6 47.2 3.0

113

M

24 573.8 2 814.2 4 539.8 295.4 269.3 30.8 54.5 3.5

Saint Kitts and

Nevis

Both 12.3 12.3 12.3 NA NA NA NA NA NA NA NA

F


M


Saint Lucia Both 21.2 19.0 21.2 37.3 340.6 1.4 5.8 0.4 3.7 0.0 0.0

F

18.1 334.8 1.1 9.7 0.2 3.6 0.0 0.1

M

19.2 346.3 0.2 2.0 0.2 3.8 0.0 0.0

Saint Vincent

and the

Grenadines

Both 21.2 19.7 21.4 56.7 683.0 4.3 24.0 0.6 7.4 0.1 0.3

F

27.5 669.5 2.2 24.4 0.3 7.3 0.0 0.3

M

29.3 696.2 2.2 23.6 0.3 7.6 0.0 0.3

Samoa Both 10.6 10.2 10.9 71.7 305.2 10.2 21.1 0.8 3.3 0.1 0.2

F

29.1 256.7 4.0 17.3 0.3 2.8 0.0 0.2

M

42.6 350.5 6.2 24.7 0.5 3.8 0.1 0.3

San Marino Both 13.4 NA 13.4 NA NA NA NA NA NA NA NA

F


M


Sao Tome and

Principe

Both 25.7 26.3 25.2 544.1 1 736.7 122.3 222.4 6.0 19.0 1.4 2.6

F

225.2 1 450.3 58.9 215.8 2.5 15.9 0.7 2.6

M

318.9 2 018.1 63.4 228.9 3.5 22.1 0.8 2.7

Saudi Arabia Both 78.4 75.1 86.7 16 146.4 544.4 1 854.7 35.2 176.7 6.0 20.7 0.4

F

7 905.3 541.1 1 028.9 39.6 86.5 5.9 11.5 0.4

M

8 241.1 547.7 825.8 30.8 90.2 6.0 9.1 0.3

Senegal Both 37.5 35.2 39.7 93 893.9 3 690.3 11 931.3 292.3 1 030.6 40.5 143.3 3.5

F

42 660.2 3 399.8 6 145.4 304.7 468.6 37.3 73.7 3.7

M

51 233.7 3 973.0 5 785.9 280.2 562.0 43.6 69.7 3.4

Serbia Both 24.3 23.0 24.7 388.2 82.8 51.5 5.2 4.1 0.9 0.5 0.1

114

F

173.2 75.6 21.6 4.5 1.8 0.8 0.2 -

M

215.0 89.6 29.8 5.9 2.3 1.0 0.3 0.1

Seychelles Both 18.7 19.0 18.6 21.4 272.7 10.5 81.6 0.2 3.0 0.1 1.0

F

8.6 224.8 4.8 74.2 0.1 2.4 0.1 0.9

M

12.7 318.7 5.8 89.0 0.1 3.5 0.1 1.1

Sierra Leone Both 20.6 20.7 20.6 63 466.9 5 563.1 7 217.0 361.5 695.0 60.9 86.0 4.3

F

30 738.8 5 405.9 3 832.8 383.0 336.6 59.2 45.6 4.6

M

32 728.1 5 719.3 3 384.3 340.0 358.4 62.6 40.3 4.1

Singapore Both 18.3 12.7 18.3 213.1 80.3 17.9 3.0 2.3 0.9 0.2 0.0

F

89.0 69.7 10.4 3.6 1.0 0.8 0.1 0.0

M

124.1 90.1 7.5 2.5 1.3 1.0 0.1 0.0

Slovakia Both 17.5 16.4 18.0 503.7 179.1 99.6 18.1 5.5 1.9 1.2 0.2

F

228.7 166.4 54.2 20.1 2.5 1.8 0.6 0.2

M

275.0 191.1 45.5 16.1 3.0 2.1 0.5 0.2

Slovenia Both 15.8 14.7 16.4 10.8 10.1 6.0 3.0 0.1 0.1 0.1 0.0

F

5.4 10.4 0.8 0.9 0.1 0.1 0.0 0.0

M

5.4 9.8 5.2 5.0 0.0 0.1 0.1 0.0

Solomon

Islands

Both 10.7 10.6 11.5 745.1 900.4 39.5 26.0 8.1 9.8 0.5 0.3

F

365.1 910.9 16.3 22.1 4.0 9.9 0.2 0.3

M

380.0 890.6 23.2 29.6 4.2 9.7 0.3 0.3

Somalia Both 29.5 29.9 28.0 385 089.7 14 714.7 22 838.9 564.4 4 241.5 162.1 272.4 6.7

F

185 858.5 14 323.6 11 821.5 587.0 2 047.4 157.8 140.9 7.0

M

199 231.2 15 099.3 11 017.3 542.0 2 194.1 166.3 131.5 6.5

South Africa Both 23.6 20.9 24.3 159 530.4 2 796.4 10 686.9 100.4 1 743.2 30.6 127.7 1.2

F

74 588.8 2 642.3 4 634.3 87.8 815.1 28.9 55.1 1.0

M

84 941.6 2 947.3 6 052.6 113.0 928.2 32.2 72.6 1.4

South Sudan Both 41.1 41.1 40.9 205 630.5 10 681.8 9 267.5 289.9 2 265.4 117.7 110.6 3.5

F

99 177.9 10 458.9 4 687.2 296.8 1 092.9 115.3 55.9 3.5

115

M

106 452.6 10 898.3 4 580.3 283.1 1 172.6 120.0 54.7 3.4

Spain Both 9.5 8.3 9.8 235.3 11.4 37.1 0.8 2.5 0.1 0.4 0.0

F

111.5 11.1 24.8 1.1 1.2 0.1 0.3 0.0

M

123.8 11.6 12.2 0.5 1.3 0.1 0.1 0.0

Sri Lanka Both 15.2 17.3 15.1 1 913.9 119.5 617.8 17.9 20.7 1.3 7.1 0.2

F

792.1 100.6 315.9 18.4 8.6 1.1 3.7 0.2

M

1 121.8 137.8 301.9 17.4 12.1 1.5 3.5 0.2

Sudan Both 47.9 48.3 46.8 346 987.7 5 841.2 15 068.4 145.6 3 829.3 64.5 178.4 1.7

F

197 254.8 6 755.9 7 456.8 146.3 2 177.0 74.6 88.4 1.7

M

149 732.9 4 957.1 7 611.6 144.8 1 652.2 54.7 90.0 1.7

Suriname Both 23.6 19.4 25.8 250.4 497.6 26.0 26.4 2.7 5.4 0.3 0.3

F

107.4 442.2 11.9 25.0 1.2 4.8 0.1 0.3

M

143.0 549.4 14.1 27.6 1.6 6.0 0.2 0.3

Sweden Both 5.9 5.4 6.1 42.0 7.2 16.6 1.5 0.5 0.1 0.2 0.0

F

16.8 5.9 4.8 0.9 0.2 0.1 0.1 0.0

M

25.2 8.4 11.8 2.0 0.3 0.1 0.1 0.0

Switzerland Both 10.2 8.6 10.4 43.3 10.0 8.4 1.0 0.5 0.1 0.1 0.0

F

19.4 9.2 6.7 1.7 0.2 0.1 0.1 0.0

M

23.9 10.7 1.7 0.4 0.3 0.1 0.0 0.0

Syrian Arab

Republic

Both 39.4 49.2 37.4 18 592.1 885.5 4 051.8 84.6 203.6 9.7 47.8 1.0

F

8 281.0 809.8 2 258.2 96.7 90.7 8.9 26.7 1.1

M

10 311.1 957.4 1 793.6 73.1 112.9 10.5 21.0 0.9

Tajikistan Both 40.0 37.8 42.8 49 135.8 4 153.5 3 787.6 200.5 536.9 45.4 44.1 2.3

F

23 175.8 4 024.1 1 867.1 203.1 253.3 44.0 21.8 2.4

M

25 959.9 4 276.3 1 920.5 198.1 283.6 46.7 22.4 2.3

Thailand Both 26.2 25.2 26.6 13 971.7 370.8 2 541.5 30.3 152.1 4.0 29.8 0.4

F

5 898.3 321.9 828.5 20.3 64.3 3.5 9.5 0.2

M

8 073.4 417.1 1 713.1 39.7 87.9 4.5 20.3 0.5

116

The former

Yugoslav

Republic of

Macedonia

Both 28.3 24.6 33.0 405.1 343.4 26.1 11.3 4.4 3.7 0.2 0.1

F

194.7 339.1 2.9 2.5 2.1 3.7 0.0 0.0

M

210.4 347.4 23.3 19.6 2.3 3.7 0.2 0.2

Timor-Leste Both 17.9 17.2 18.2 7 316.2 3 549.9 295.4 84.7 80.1 38.8 3.5 1.0

F

3 747.9 3 710.8 161.7 94.6 41.0 40.6 1.9 1.1

M

3 568.3 3 395.4 133.7 75.1 39.0 37.1 1.6 0.9

Togo Both 32.7 35.1 31.2 65 377.8 5 560.0 9 176.9 457.6 718.5 61.1 110.3 5.5

F

28 670.6 4 891.1 5 227.8 522.8 315.2 53.8 62.8 6.3

M

36 707.2 6 224.8 3 949.1 392.7 403.3 68.4 47.5 4.7

Tonga Both 10.1 9.9 10.2 41.9 330.0 5.6 21.4 0.5 3.6 0.1 0.2

F

23.9 386.3 2.2 17.3 0.3 4.2 0.0 0.2

M

18.0 276.5 3.4 25.3 0.2 3.0 0.0 0.3

Trinidad and

Tobago

Both 22.0 20.9 22.4 464.1 490.8 27.0 14.3 5.0 5.3 0.3 0.2

F

213.9 459.9 7.0 7.5 2.3 5.0 0.1 0.1

M

250.2 520.7 20.0 21.0 2.7 5.7 0.2 0.2

Tunisia Both 35.7 35.5 35.7 5 505.9 523.6 694.4 41.5 60.0 5.7 7.9 0.5

F

2 825.9 550.1 414.5 50.6 30.8 6.0 4.8 0.6

M

2 680.0 498.4 280.0 32.7 29.2 5.4 3.1 0.4

Turkey Both 42.0 43.2 41.2 17 035.1 251.5 2 769.7 20.8 184.7 2.7 31.2 0.2

F

8 288.6 250.6 1 223.3 18.7 89.9 2.7 13.6 0.2

M

8 746.5 252.2 1 546.4 22.7 94.7 2.7 17.5 0.3

Turkmenistan Both 19.0 18.0 24.2 19 238.8 2 712.4 641.9 61.9 210.6 29.7 7.6 0.7

F

8 186.1 2 343.3 273.3 53.4 89.6 25.7 3.2 0.6

M

11 052.7 3 070.5 368.7 70.3 121.0 33.6 4.4 0.8

Tuvalu Both 11.4 11.4 NA NA NA NA NA NA NA NA NA

F


117

M


Uganda Both 48.4 47.1 48.7 405 794.7 5 270.8 54 975.3 450.3 4 456.2 57.9 659.9 5.4

F

187 496.0 4 917.1 26 635.4 439.5 2 057.7 54.0 319.6 5.3

M

218 298.8 5 618.0 28 339.8 460.9 2 398.5 61.7 340.3 5.5

Ukraine Both 18.3 16.7 19.4 5 379.3 230.5 494.5 11.2 58.1 2.5 4.8 0.1

F

2 864.5 253.2 251.9 11.7 31.0 2.7 2.5 0.1

M

2 514.7 209.1 242.7 10.6 27.1 2.3 2.3 0.1

United Arab

Emirates

Both 39.4 40.2 37.2 826.1 178.1 63.3 7.7 8.8 1.9 0.6 0.1

F

464.4 204.5 21.0 5.2 5.0 2.2 0.2 0.0

M

361.7 152.7 42.3 10.1 3.8 1.6 0.4 0.1

United

Kingdom

Both 10.5 8.4 10.6 1,201.2 30.0 218.7 2.9 13.1 0.3 2.5 0.0

F

551.8 28.3 98.1 2.6 6.0 0.3 1.1 0.0

M

649.4 31.7 120.7 3.1 7.1 0.3 1.4 0.0

United

Republic of

Tanzania

Both 25.6 26.4 25.1 349 813.8 3 623.0 38 743.3 251.6 3 841.4 39.8 461.5 3.0

F

170 825.3 3 572.0 19 573.9 255.0 1 875.9 39.2 233.0 3.0

M

178 988.5 3 673.0 19 169.4 248.2 1 965.5 40.3 228.5 3.0

United States

of America

Both 7.4 6.7 7.6 4 106.7 20.9 780.7 1.9 44.0 0.2 7.9 0.0

F

1 880.0 19.6 359.9 1.8 20.2 0.2 3.6 0.0

M

2 226.8 22.2 420.8 2.0 23.8 0.2 4.3 0.0

Uruguay Both 8.6 8.0 8.7 132.5 55.3 10.8 2.2 1.4 0.6 0.1 0.0

F

57.9 49.3 3.0 1.3 0.6 0.5 0.0 0.0

M

74.7 61.1 7.8 3.1 0.8 0.7 0.1 0.0

Uzbekistan Both 25.3 22.3 28.9 42 403.4 1 331.7 6 865.0 122.3 462.4 14.5 81.3 1.4

F

19 002.0 1 238.2 3 183.6 116.4 207.2 13.5 37.7 1.4

M

23 401.4 1 418.6 3 681.4 127.9 255.2 15.5 43.6 1.5

Vanuatu Both 10.3 10.1 11.0 244.9 711.6 18.5 29.2 2.7 7.8 0.2 0.3

118

F

115.3 693.7 6.7 22.0 1.3 7.6 0.1 0.3

M

129.6 728.3 11.8 35.7 1.4 7.9 0.1 0.4

Venezuela

(Bolivarian

Republic of)

Both 15.8 14.6 16.8 13 427.7 451.5 945.7 16.2 146.6 4.9 11.2 0.2

F

5 822.2 400.5 399.7 14.0 63.6 4.4 4.7 0.2

M

7 605.5 500.2 546.0 18.3 83.1 5.5 6.5 0.2

Viet Nam Both 29.7 26.6 30.1 89 655.6 1 155.2 2 679.9 19.1 979.7 12.6 30.0 0.2

F

35 201.1 958.1 875.3 13.0 384.5 10.5 9.4 0.1

M

54 454.5 1 332.5 1 804.7 24.7 595.2 14.6 20.7 0.3

Yemen Both 45.0 46.5 44.3 221 360.4 5 431.9 6 609.1 94.0 2 427.7 59.6 77.4 1.1

F

126 048.6 6 323.9 3 465.8 100.6 1 382.6 69.4 40.7 1.2

M

95 311.9 4 578.0 3 143.4 87.7 1 045.1 50.2 36.7 1.0

Zambia Both 24.7 25.2 23.8 103 337.7 3 664.3 9 846.4 211.2 1 134.4 40.2 117.7 2.5

F

49 165.1 3 519.4 4 660.9 201.2 539.9 38.6 55.7 2.4

M

54 172.6 3 806.6 5 185.5 221.2 594.5 41.8 62.0 2.6

Zimbabwe Both 19.4 19.5 19.1 69 229.9 2 726.2 9 044.7 218.3 760.2 29.9 108.5 2.6

F

34 940.5 2 765.4 4 816.1 233.0 383.8 30.4 57.8 2.8

M

34 289.5 2 687.4 4 228.6 203.6 376.4 29.5 50.7 2.4

NA, not available

F, females; M, males

119

Table 5. Exposure of children to household PM2.5 and burden of disease, by country, 2016

Country Sex Households

that rely

primarily on

clean

cooking fuels

(%)

Households

that rely

primarily on

polluting

fuels (%)

No. of

DALYs (< 5

years)

DALYs

rate per 100

000 (< 5

year)

No. of

DALYs (5

- 14 years)

DALYs

rate per 100

000 (5 - 14

years)

No. of

deaths (< 5

years)

Death rate

per 100 000

(< 5 years)

No. of

deaths (5 - 14

years)

Death rate

per 100 000

(5 - 14 years)

Afghanistan Both <5 68 536 072.8 10 244.4 38 386.5 385.1 5 895.7 112.7 456.2 4.6

F

280 259.1 11 015.8 23 334.5 480.5 3 084.9 121.3 277.7 5.7

M

255 813.7 9 514.4 15 052.0 294.5 2 810.8 104.5 178.5 3.5

Albania Both 77 <5 961.1 541.9 157.8 46.2 10.4 5.8 1.8 0.5

F

455.8 531.8 116.4 71.2 4.9 5.7 1.4 0.8

M

505.4 551.3 41.4 23.3 5.4 5.9 0.4 0.3

Algeria Both 93 7 20 107.2 427.9 885.4 12.5 220.2 4.7 10.3 0.1

F

11 836.2 514.7 493.3 14.2 129.7 5.6 5.7 0.2

M

8 271.0 344.7 392.1 10.9 90.5 3.8 4.5 0.1

Andorra Both >95 <5 NA NA NA NA NA NA NA NA

F


M


Angola Both <5 52 611 101.3 11 580.2 29 091.3 352.5 6 723.9 127.4 346.9 4.2

F

320 272.3 12 190.6 15 335.7 368.1 3 526.9 134.2 182.8 4.4

M

290 829.1 10 975.1 13 755.6 336.6 3 197.0 120.6 164.2 4.0

Antigua and

Barbuda

Both >95 <5 0.5 5.8 0.3 1.6 0.0 0.1 0.0 0.0

F

0.2 4.6 0.0 0.1 0.0 0.0 0.0 0.0

M

0.3 7.1 0.2 3.0 0.0 0.1 0.0 0.0

Argentina Both >95 <5 979.7 26.2 99.4 1.4 10.7 0.3 1.2 0.0

F

445.7 24.3 55.8 1.6 4.8 0.3 0.7 0.0

120

M

534.1 28.1 43.5 1.2 5.8 0.3 0.5 0.0

Armenia Both >95 <5 169.1 83.6 21.2 5.6 1.8 0.9 0.2 0.1

F

78.2 82.6 10.6 6.0 0.9 0.9 0.1 0.1

M

90.9 84.6 10.6 5.2 1.0 0.9 0.1 0.1

Australia Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Austria Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Azerbaijan Both >95 <5 3 262.8 366.4 286.8 21.0 35.6 4.0 3.4 0.2

F

1 677.6 404.9 140.6 22.3 18.3 4.4 1.6 0.3

M

1 585.1 332.9 146.2 20.0 17.3 3.6 1.7 0.2

Bahamas Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Bahrain Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Bangladesh Both <5 82 791 065.4 5 192.2 33 263.4 104.4 8 655.5 56.8 391.7 1.2

F

355 050.2 4 764.0 20 364.5 130.7 3 885.5 52.1 241.0 1.5

M

436 015.2 5 602.4 12 898.9 79.3 4 770.0 61.3 150.8 0.9

Barbados Both >95 <5 0.7 4.0 0.3 0.9 0.0 0.0 0.0 0.0

F

0.3 3.9 0.1 0.3 0.0 0.0 0.0 0.0

M

0.4 4.2 0.3 1.4 0.0 0.0 0.0 0.0

Belarus Both >95 <5 52.1 9.0 8.6 0.9 0.5 0.1 0.1 0.0

F

23.0 8.2 4.5 0.9 0.2 0.1 0.0 0.0

M

29.1 9.8 4.1 0.8 0.3 0.1 0.0 0.0

121

Belgium Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Belize Both 85 <5 124.2 308.1 14.4 18.8 1.3 3.3 0.2 0.2

F

61.2 306.9 6.3 16.7 0.7 3.3 0.1 0.2

M

63.0 309.3 8.1 20.7 0.7 3.4 0.1 0.2

Benin Both 6 94 285 483.2 16 079.5 28 648.8 993.0 3 139.6 176.8 342.7 11.9

F

150 504.4 17 218.8 15 313.6 1 076.7 1 656.1 189.5 183.0 12.9

M

134 978.8 14 974.7 13 335.2 911.7 1 483.5 164.6 159.7 10.9

Bhutan Both 52 <5 2 332.0 3 340.2 227.3 156.7 25.5 36.5 2.7 1.9

F

1 049.3 3 057.2 130.9 183.3 11.5 33.4 1.6 2.2

M

1 282.7 3 613.9 96.5 130.9 14.0 39.5 1.2 1.6

Bolivia

(Plurinational State

of)

Both 80 <5 23 378.9 1 967.1 2 935.6 127.7 256.1 21.5 35.2 1.5

F

10 966.6 1 884.1 1 503.4 133.1 120.1 20.6 18.0 1.6

M

12 412.3 2 046.7 1 432.2 122.5 135.9 22.4 17.2 1.5

Bosnia and

Herzegovina

Both 63 <5 291.9 186.3 30.1 8.8 3.1 2.0 0.2 0.1

F

136.3 179.4 13.2 7.9 1.4 1.9 0.1 0.1

M

155.6 192.7 16.9 9.6 1.6 2.0 0.1 0.1

Botswana Both 64 <5 5 980.2 2 307.0 599.3 132.5 65.6 25.3 7.2 1.6

F

2 885.6 2 249.3 291.2 129.7 31.6 24.7 3.5 1.5

M

3 094.6 2 363.6 308.1 135.2 34.0 25.9 3.7 1.6

Brazil Both >95 <5 19 393.6 130.0 1 711.1 5.5 210.8 1.4 19.6 0.1

F

8 962.2 123.0 888.5 5.8 97.4 1.3 10.1 0.1

M

10 431.4 136.7 822.6 5.2 113.5 1.5 9.5 0.1

Brunei Darussalam Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

122

Bulgaria Both 89 <5 400.5 123.5 74.2 10.9 4.3 1.3 0.9 0.1

F

179.1 113.7 37.1 11.2 1.9 1.2 0.4 0.1

M

221.4 132.8 37.1 10.6 2.4 1.4 0.4 0.1

Burkina Faso Both 9 91 365 511.5 11 349.0 45 373.5 865.5 4 019.0 124.8 543.0 10.4

F

173 377.7 10 959.8 25 996.9 1 009.9 1 907.4 120.6 311.0 12.1

M

192 133.8 11 724.7 19 376.6 726.2 2 111.6 128.9 232.0 8.7

Burundi Both <5 >95 253 667.5 13 341.5 44 100.4 1 563.2 2 786.7 146.6 529.3 18.8

F

121 156.3 12 827.4 24 410.2 1 724.6 1 331.5 141.0 292.9 20.7

M

132 511.3 13 848.9 19 690.1 1 400.6 1 455.2 152.1 236.4 16.8

Cabo Verde Both 71 <5 749.1 1,371.2 34.9 31.5 8.2 15.0 0.4 0.4

F

338.5 1,254.0 17.9 32.5 3.7 13.7 0.2 0.4

M

410.6 1,485.6 17.0 30.5 4.5 16.2 0.2 0.4

Cambodia Both <5 82 85 859.8 4 874.9 7 697.8 241.6 937.4 53.2 90.6 2.8

F

38 932.6 4 493.1 3 506.9 224.3 425.3 49.1 41.2 2.6

M

46 927.2 5 244.6 4 190.9 258.2 512.1 57.2 49.4 3.0

Cameroon Both <5 77 467 990.2 12 301.8 85 522.5 1 367.9 5 143.3 135.2 1 023.5 16.4

F

211 652.8 11 239.6 43 030.6 1 387.1 2 327.0 123.6 515.0 16.6

M

256 337.4 13 342.8 42 491.9 1 348.9 2 816.3 146.6 508.5 16.1

Canada Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Central African

Republic

Both <5 >95 172 005.1 23 547.7 7 977.6 629.0 1 888.8 258.6 95.3 7.5

F

87 895.5 24 102.4 4 355.6 682.3 965.9 264.9 52.0 8.1

M

84 109.6 22 994.7 3 622.0 575.0 922.9 252.3 43.3 6.9

Chad Both <5 >95 932 652.8 34 978.1 48 074.9 1 149.6 10 284.5 385.7 577.5 13.8

F

448 919.3 33 968.7 27 053.5 1 303.9 4 952.6 374.7 325.1 15.7

M

483 733.4 35 970.1 21 021.3 997.7 5 332.0 396.5 252.4 12.0

Chile Both 92 8 520.2 43.9 39.3 1.6 5.6 0.5 0.4 0.0

123

F

233.4 40.2 20.7 1.7 2.5 0.4 0.2 0.0

M

286.8 47.5 18.6 1.5 3.1 0.5 0.2 0.0

China Both 59 <5 641 169.7 744.0 47 970.3 29.4 6 973.0 8.1 530.3 0.3

F

283 636.2 709.0 28 360.7 37.6 3 074.5 7.7 302.9 0.4

M

357 533.4 774.3 19 609.5 22.4 3 898.6 8.4 227.4 0.3

Colombia Both 92 8 9 831.5 264.9 964.0 12.2 107.4 2.9 11.5 0.1

F

4 571.0 251.7 455.5 11.8 49.9 2.7 5.4 0.1

M

5 260.6 277.5 508.6 12.6 57.5 3.0 6.1 0.2

Comoros Both 9 91 16 620.7 13 950.0 902.5 453.7 182.6 153.2 10.8 5.4

F

8 150.6 13 945.8 457.2 467.8 89.6 153.2 5.4 5.6

M

8 470.1 13 954.1 445.4 440.2 93.0 153.2 5.3 5.3

Congo Both <5 76 59 422.8 7 209.3 4 250.9 315.3 652.3 79.1 51.0 3.8

F

29 124.4 7 134.4 2 502.1 373.1 320.0 78.4 30.0 4.5

M

30 298.3 7 282.7 1 748.8 258.1 332.3 79.9 21.0 3.1

Cook Islands Both 84 <5 NA NA NA NA NA NA NA NA

F


M


Costa Rica Both 93 7 216.4 62.6 27.1 3.8 2.3 0.7 0.3 0.0

F

101.0 59.8 15.8 4.5 1.1 0.6 0.2 0.0

M

115.4 65.3 11.3 3.1 1.2 0.7 0.1 0.0

Côte d'Ivoire Both <5 82 615 560.0 15 944.6 89 345.5 1 434.5 6 760.8 175.1 1 073.6 17.2

F

272 639.3 14 220.9 51 471.6 1 655.4 2 995.7 156.3 618.4 19.9

M

342 920.7 17 645.1 37 873.9 1 214.4 3 765.2 193.7 455.2 14.6

Croatia Both 93 7 53.0 27.0 6.7 1.6 0.6 0.3 0.1 0.0

F

21.8 22.8 0.5 0.3 0.2 0.2 0.0 0.0

M

31.2 30.9 6.2 2.9 0.3 0.3 0.1 0.0

Cuba Both 79 <5 1 300.7 204.4 96.7 7.9 14.1 2.2 1.0 0.1

F

598.4 193.3 63.2 10.7 6.5 2.1 0.7 0.1

124

M

702.2 214.9 33.4 5.3 7.6 2.3 0.3 0.1

Cyprus Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Czechia Both >95 <5 57.0 10.7 16.1 1.5 0.6 0.1 0.2 0.0

F

23.5 9.0 6.6 1.3 0.3 0.1 0.1 0.0

M

33.5 12.2 9.5 1.7 0.4 0.1 0.1 0.0

Democratic

People's Republic

of Korea

Both <5 89 51 126.5 2 961.4 3 388.3 95.0 558.0 32.3 39.0 1.1

F

22 540.8 2 673.5 1 948.9 111.6 245.6 29.1 22.4 1.3

M

28 585.7 3 236.2 1 439.5 79.0 312.4 35.4 16.6 0.9

Democratic

Republic of the

Congo

Both <5 >95 2 447 746.5 16 888.0 185 810.5 844.7 26 835.8 185.2 2 237.9 10.2

F

1 219 830.4 16 999.8 106 474.9 975.1 13 378.4 186.4 1 281.9 11.7

M

1 227 916.2 16 778.3 79 335.6 716.2 13 457.4 183.9 956.0 8.6

Denmark Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Djibouti Both <5 88 9 180.7 8 975.9 2 082.3 1 066.8 100.4 98.2 25.1 12.9

F

4 237.7 8 403.8 1 059.7 1 099.0 46.4 91.9 12.8 13.2

M

4 943.0 9 532.2 1 022.6 1 035.2 54.1 104.3 12.3 12.5

Dominica Both 91 9 NA NA NA NA NA NA NA NA

F


M


Dominican

Republic

Both 90 <5 7 409.4 699.3 381.7 18.2 81.2 7.7 4.4 0.2

F

2 952.5 569.0 196.5 19.1 32.3 6.2 2.3 0.2

M

4 456.9 824.3 185.2 17.4 48.8 9.0 2.1 0.2

125

Ecuador Both >95 <5 4 218.9 261.9 325.8 10.5 46.2 2.9 3.9 0.1

F

1 950.9 247.9 167.4 11.1 21.4 2.7 2.0 0.1

M

2 268.0 275.2 158.4 10.0 24.8 3.0 1.9 0.1

Egypt Both >95 <5 15 687.4 121.8 3 820.6 20.0 171.4 1.3 45.5 0.2

F

6 859.8 110.0 1 853.4 20.0 75.0 1.2 22.0 0.2

M

8 827.6 133.0 1 967.2 20.0 96.4 1.5 23.5 0.2

El Salvador Both 86 <5 2 740.1 475.3 367.4 31.0 29.9 5.2 4.5 0.4

F

1 067.1 379.0 145.4 25.0 11.6 4.1 1.8 0.3

M

1 673.0 567.2 222.0 36.7 18.3 6.2 2.7 0.5

Equatorial Guinea Both <5 66 22 068.8 12,149.8 2,029.8 739.2 242.0 133.2 24.3 8.8

F

9 464.0 10 556.8 1 171.0 864.3 103.9 115.8 14.0 10.3

M

12 604.9 13 702.3 858.9 617.4 138.2 150.2 10.3 7.4

Eritrea Both <5 84 67 043.0 9 014.3 8 238.4 614.8 736.1 99.0 98.2 7.3

F

31 001.5 8 514.6 3 980.0 605.8 340.4 93.5 47.4 7.2

M

36 041.5 9 493.6 4 258.3 623.5 395.7 104.2 50.9 7.4

Estonia Both 93 7 13.1 19.4 7.8 5.4 0.1 0.2 0.1 0.1

F

6.1 18.6 3.3 4.7 0.1 0.2 0.0 0.0

M

7.0 20.1 4.5 6.0 0.1 0.2 0.0 0.1

eSwatini Both 50 50 14 882.5 8 286.2 1 432.7 444.1 163.0 90.8 17.1 5.3

F

6 941.0 7 780.0 784.2 487.8 76.0 85.2 9.4 5.8

M

7 941.5 8 785.8 648.4 400.6 87.0 96.2 7.7 4.8

Ethiopia Both <5 >95 1 532 998.5 10 100.7 215 306.3 800.1 16 851.7 111.0 2 583.7 9.6

F

683 480.8 9 141.6 106 266.4 798.3 7 513.1 100.5 1 274.3 9.6

M

849 517.7 11 031.9 109 039.9 801.8 9 338.6 121.3 1 309.4 9.6

Fiji Both <5 60 1 801.2 2 080.3 230.9 135.5 19.6 22.7 2.7 1.6

F

872.1 2 071.4 124.3 150.9 9.5 22.6 1.5 1.8

M

929.1 2 088.8 106.5 121.1 10.1 22.8 1.3 1.4

Finland Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

126

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

France Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Gabon Both 79 <5 6 915.9 2 527.6 949.1 217.5 75.9 27.7 11.6 2.7

F

3 141.4 2 321.8 489.8 226.5 34.5 25.5 6.0 2.8

M

3 774.5 2 729.0 459.4 208.7 41.4 29.9 5.6 2.6

Gambia Both <5 >95 36 698.0 10 180.3 3 274.6 576.8 404.1 112.1 39.1 6.9

F

17 412.6 9 757.6 1 959.7 696.3 191.9 107.5 23.4 8.3

M

19 285.3 10 594.6 1 315.0 459.4 212.2 116.6 15.7 5.5

Georgia Both 78 <5 766.2 282.5 260.6 55.1 8.3 3.1 3.1 0.7

F

381.7 292.2 104.7 46.9 4.1 3.2 1.2 0.5

M

384.6 273.5 156.0 62.5 4.2 3.0 1.9 0.7

Germany Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Ghana Both <5 78 272 401.1 6 667.9 27 499.1 403.0 2 992.1 73.2 329.5 4.8

F

121 158.7 6 057.1 14 732.9 441.5 1 330.6 66.5 176.5 5.3

M

151 242.3 7 253.8 12 766.2 366.1 1 661.5 79.7 153.0 4.4

Greece Both 94 6 85.5 18.0 12.8 1.1 0.9 0.2 0.1 0.0

F

39.0 17.0 6.3 1.1 0.4 0.2 0.1 0.0

M

46.6 19.0 6.5 1.1 0.5 0.2 0.1 0.0

Grenada Both >95 <5 13.7 138.9 0.4 2.2 0.1 1.5 0.0 0.0

F

6.4 134.0 0.0 0.4 0.1 1.5 0.0 0.0

M

7.3 143.5 0.4 4.0 0.1 1.6 0.0 0.0

Guatemala Both <5 55 71 014.1 3 510.9 5 243.6 134.8 776.9 38.4 62.6 1.6

F

32 910.2 3 327.5 2 746.5 144.2 360.1 36.4 32.7 1.7

127

M

38 103.9 3 686.4 2 497.2 125.7 416.9 40.3 29.9 1.5

Guinea Both <5 >95 339 682.9 17 130.7 36 814.8 1 122.0 3 732.4 188.2 440.8 13.4

F

160 848.1 16 327.5 23 119.8 1 419.1 1 768.1 179.5 276.6 17.0

M

178 834.9 17 923.7 13 695.0 829.0 1 964.3 196.9 164.2 9.9

Guinea-Bissau Both <5 >95 51 497.1 17 706.4 2 846.0 612.3 567.9 195.3 34.1 7.3

F

24 469.1 16 900.6 1 545.1 665.2 269.8 186.3 18.5 8.0

M

27 028.0 18 505.3 1 300.8 559.5 298.1 204.1 15.6 6.7

Guyana Both 74 <5 931.1 1 218.4 156.8 103.8 10.2 13.3 1.9 1.2

F

431.6 1 158.8 73.2 99.6 4.7 12.7 0.9 1.2

M

499.5 1 275.0 83.6 107.8 5.5 13.9 1.0 1.3

Haiti Both <5 >95 189 093.2 15 330.7 13 926.5 584.4 2 075.7 168.3 166.6 7.0

F

85 223.7 14 094.7 6 174.6 526.4 935.6 154.7 73.9 6.3

M

103 869.6 16 519.2 7 751.9 640.5 1 140.1 181.3 92.7 7.7

Honduras Both 53 <5 14 417.5 1 515.7 556.3 27.9 157.5 16.6 6.4 0.3

F

6 448.3 1 383.4 169.9 17.4 70.4 15.1 1.9 0.2

M

7 969.2 1 642.8 386.4 38.1 87.1 18.0 4.6 0.5

Hungary Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Iceland Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

India Both <5 59 6 098 660.0 5 082.3 398 523.0 157.3 66 890.5 55.7 4 711.7 1.9

F

3 286 253.8 5 780.6 232 061.8 193.8 36 073.1 63.5 2 758.9 2.3

M

2 812 406.5 4 453.7 166 461.2 124.6 30 817.4 48.8 1 952.8 1.5

Indonesia Both 58 <5 628 573.7 2 532.3 36 982.9 78.1 6 862.4 27.6 430.8 0.9

F

308 557.5 2 544.4 18 211.6 78.7 3 370.4 27.8 211.2 0.9

M

320 016.2 2 520.8 18 771.4 77.4 3 492.0 27.5 219.6 0.9

128

Iran (Islamic

Republic of)

Both >95 <5 4 347.9 63.7 178.3 1.5 47.4 0.7 2.0 0.0

F

2 299.8 68.9 95.8 1.6 25.1 0.8 1.1 0.0

M

2 048.1 58.8 82.5 1.3 22.3 0.6 0.9 0.0

Iraq Both >95 <5 14 770.1 257.4 292.2 3.1 161.6 2.8 3.3 0.0

F

4 824.8 173.0 170.5 3.8 52.9 1.9 1.9 0.0

M

9 945.3 337.3 121.7 2.5 108.7 3.7 1.4 0.0

Ireland Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Israel Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Italy Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Jamaica Both 90 <5 267.6 130.5 39.8 8.7 2.9 1.4 0.4 0.1

F

121.7 122.4 21.3 9.5 1.3 1.3 0.2 0.1

M

145.8 138.1 18.6 8.0 1.6 1.5 0.2 0.1

Japan Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Jordan Both >95 <5 392.0 31.9 30.0 1.4 4.3 0.3 0.3 0.0

F

187.9 31.3 18.6 1.7 2.0 0.3 0.2 0.0

M

204.1 32.6 11.4 1.0 2.2 0.4 0.1 0.0

Kazakhstan Both 95 5 2 738.7 137.1 181.1 6.2 29.8 1.5 2.1 0.1

F

1 233.6 127.1 94.1 6.6 13.4 1.4 1.1 0.1

M

1 505.1 146.7 87.0 5.8 16.4 1.6 1.0 0.1

Kenya Both <5 87 504 366.5 7 181.3 31 760.7 248.3 5 536.8 78.8 380.6 3.0

129

F

244 476.8 7 034.4 13 165.4 207.5 2 684.7 77.2 157.4 2.5

M

259 889.7 7 325.1 18 595.3 288.5 2 852.1 80.4 223.2 3.5

Kiribati Both 6 94 1 420.1 9 803.2 80.4 317.4 15.6 107.4 0.9 3.7

F

683.2 9 670.0 24.5 197.6 7.5 105.9 0.3 2.3

M

736.9 9 929.9 55.9 431.9 8.1 108.9 0.7 5.1

Kuwait Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Kyrgyzstan Both 81 <5 11 263.5 1 481.1 453.0 40.6 123.1 16.2 5.2 0.5

F

5 201.8 1 406.0 214.0 39.2 56.8 15.4 2.4 0.4

M

6 061.8 1 552.2 238.9 41.9 66.2 17.0 2.8 0.5

Lao People's

Democratic

Republic

Both 6 94 90 213.0 11 783.1 6 330.0 426.9 988.7 129.1 74.9 5.1

F

43 609.0 11 633.1 2 997.9 412.5 478.2 127.6 35.5 4.9

M

46 604.0 11 927.1 3 332.1 440.7 510.4 130.6 39.5 5.2

Latvia Both 95 5 30.7 31.7 1.6 0.8 0.3 0.3 0.0 0.0

F

14.6 31.2 0.8 0.8 0.2 0.3 0.0 0.0

M

16.0 32.1 0.8 0.7 0.2 0.3 0.0 0.0

Lebanon Both NA NA NA NA NA NA NA NA NA NA

F


M


Lesotho Both <5 64 34 605.3 12 103.2 2 399.1 482.6 379.0 132.5 28.7 5.8

F

17 471.0 12 306.8 1 393.5 563.4 191.3 134.8 16.7 6.7

M

17 134.3 11 902.4 1 005.5 402.6 187.6 130.3 12.0 4.8

Liberia Both <5 >95 89 766.3 12 546.2 7 222.4 588.4 983.2 137.4 87.0 7.1

F

44 701.3 12 769.7 4 082.6 679.5 489.7 139.9 49.1 8.2

M

45 065.0 12 332.1 3 139.7 501.0 493.6 135.1 37.8 6.0

Libya Both NA


130

F


M


Lithuania Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Luxembourg Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Madagascar Both <5 >95 335 527.3 8 903.3 42 347.8 650.4 3 680.3 97.7 506.9 7.8

F

165 601.0 8 898.1 20 830.2 643.6 1 817.2 97.6 248.8 7.7

M

169 926.2 8 908.3 21 517.6 657.1 1 863.1 97.7 258.1 7.9

Malawi Both <5 >95 230 369.8 7 921.2 13 249.1 259.7 2 526.6 86.9 158.2 3.1

F

106 489.4 7 407.4 6 170.7 243.3 1 167.9 81.2 73.3 2.9

M

123 880.4 8 423.4 7 078.4 276.0 1 358.7 92.4 84.8 3.3

Malaysia Both >95 <5 993.0 38.0 337.5 6.7 10.7 0.4 4.0 0.1

F

427.3 33.9 160.0 6.5 4.6 0.4 1.9 0.1

M

565.7 41.8 177.5 6.8 6.1 0.5 2.1 0.1

Maldives Both 94 6 29.5 75.6 4.2 6.9 0.3 0.8 0.0 0.1

F

15.9 85.0 2.4 8.2 0.2 0.9 0.0 0.1

M

13.6 66.9 1.8 5.8 0.1 0.7 0.0 0.1

Mali Both <5 >95 554 968.1 16 655.1 36 885.2 699.1 6 105.1 183.2 439.8 8.3

F

282 341.5 17 255.2 21 778.6 838.9 3 107.1 189.9 259.7 10.0

M

272 626.7 16 076.1 15 106.6 563.7 2 998.0 176.8 180.1 6.7

Malta Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Marshall Islands Both 65 <5 NA NA NA NA NA NA NA NA

F


131

M


Mauritania Both <5 53 67 277.8 10 269.7 2 955.2 276.2 740.3 113.0 35.1 3.3

F

27 641.6 8 597.0 1 738.2 329.9 304.2 94.6 20.6 3.9

M

39 636.2 11 881.8 1 217.0 224.1 436.1 130.7 14.4 2.7

Mauritius Both 93 7 86.9 127.6 15.3 9.0 0.9 1.4 0.2 0.1

F

36.8 110.3 9.4 11.2 0.4 1.2 0.1 0.1

M

50.1 144.2 6.0 6.9 0.5 1.6 0.1 0.1

Mexico Both 85 <5 49 786.2 429.9 2 060.9 9.0 544.1 4.7 24.4 0.1

F

23 025.6 406.9 1 010.1 9.0 251.7 4.4 11.9 0.1

M

26 760.7 451.9 1 050.8 9.0 292.4 4.9 12.5 0.1

Micronesia

(Federated States

of)

Both <5 88 587.9 5 048.6 52.7 223.7 6.4 55.3 0.6 2.6

F

269.8 4 792.5 23.1 203.7 3.0 52.5 0.3 2.4

M

318.1 5 288.2 29.6 242.4 3.5 57.9 0.3 2.9

Monaco Both >95 <5 NA NA NA NA NA NA NA NA

F


M


Mongolia Both <5 57 6 965.2 1 894.9 624.9 120.3 75.9 20.6 7.2 1.4

F

2 508.5 1 382.8 273.6 106.5 27.3 15.1 3.2 1.2

M

4 456.7 2 393.8 351.2 133.7 48.6 26.1 4.1 1.6

Montenegro Both 69 <5 29.7 81.8 13.8 17.6 0.3 0.8 0.1 0.2

F

13.1 74.2 7.5 20.0 0.1 0.7 0.1 0.2

M

16.6 88.9 6.3 15.4 0.2 0.9 0.1 0.1

Morocco Both >95 <5 7 844.1 223.6 325.9 5.2 85.8 2.4 3.8 0.1

F

3 571.4 209.2 181.6 6.0 39.1 2.3 2.1 0.1

M

4 272.7 237.1 144.3 4.5 46.7 2.6 1.7 0.1

Mozambique Both <5 >95 541 134.6 10 932.0 46 454.2 578.6 5 925.9 119.7 559.7 7.0

F

265 861.7 10 823.9 23 688.1 591.9 2 912.0 118.6 285.2 7.1

132

M

275 272.9 11 038.5 22 766.1 565.4 3,013.9 120.9 274.4 6.8

Myanmar Both <5 82 404 645.6 8 917.2 29 751.4 299.4 4 436.5 97.8 353.5 3.6

F

180 837.7 8 025.9 11 230.6 227.3 1 985.8 88.1 133.2 2.7

M

223 807.9 9 796.3 18 520.8 370.5 2 450.7 107.3 220.4 4.4

Namibia Both <5 58 21 020.8 6 108.6 1 863.9 326.6 230.8 67.1 22.4 3.9

F

9 986.7 5 842.9 882.9 309.9 109.6 64.1 10.6 3.7

M

11 034.0 6 370.7 981.0 343.2 121.2 70.0 11.8 4.1

Nauru Both 91 9 NA NA NA NA NA NA NA NA

F


M


Nepal Both <5 72 130 634.5 4 739.7 8 797.7 136.9 1 429.0 51.8 103.5 1.6

F

58 115.5 4 352.2 4 660.9 148.8 635.7 47.6 55.0 1.8

M

72 519.0 5 103.8 4 136.8 125.6 793.3 55.8 48.6 1.5

Netherlands Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

New Zealand Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Nicaragua Both 52 <5 15 219.7 2 548.2 1 067.6 87.7 166.5 27.9 12.9 1.1

F

5 848.9 2 002.6 534.8 90.5 64.0 21.9 6.4 1.1

M

9 370.8 3 070.4 532.9 85.0 102.5 33.6 6.4 1.0

Niger Both <5 >95 823 082.0 19 514.2 114 793.9 1 861.6 9 079.8 215.3 1 381.6 22.4

F

418 807.2 20 295.2 61 999.4 2 051.9 4 623.2 224.0 746.1 24.7

M

404 274.8 18 766.0 52 794.6 1 678.8 4 456.6 206.9 635.5 20.2

Nigeria Both 5 95 6 950 066.0 21 854.5 772 212.4 1 538.4 76 505.4 240.6 9 221.0 18.4

F

3 204 320.0 20 665.3 458 454.4 1 865.4 35 295.5 227.6 5 472.3 22.3

M

3 745 746.0 22 986.1 313 757.9 1 224.7 41 209.8 252.9 3 748.7 14.6

133

Niue Both 93 7 NA NA NA NA NA NA NA NA

F


M


Norway Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Oman Both 95 5 250.4 62.6 23.5 4.1 2.7 0.7 0.3 0.0

F

144.2 74.2 12.9 4.5 1.6 0.8 0.1 0.0

M

106.3 51.6 10.6 3.7 1.1 0.6 0.1 0.0

Pakistan Both <5 57 2 365 748.2 9 477.2 110 327.6 259.9 25 933.0 103.9 1 316.7 3.1

F

1 197 080.8 9 973.7 44 879.4 219.7 13 127.7 109.4 534.1 2.6

M

1 168 667.5 9 017.3 65 448.2 297.2 12 805.3 98.8 782.7 3.6

Palau Both 87 <5 NA NA NA NA NA NA NA NA

F


M


Panama Both 89 <5 1 907.1 491.1 165.4 22.8 20.8 5.4 2.0 0.3

F

828.2 435.7 79.0 22.2 9.0 4.8 0.9 0.3

M

1 079.0 544.1 86.4 23.3 11.8 5.9 1.0 0.3

Papua New Guinea Both <5 87 89 471.3 8 663.9 6 050.7 318.5 978.4 94.7 71.1 3.7

F

39 006.5 7 821.9 2 230.1 242.5 426.4 85.5 26.1 2.8

M

50 464.8 9 450.2 3 820.6 389.7 552.0 103.4 45.1 4.6

Paraguay Both 66 <5 7 976.4 1 187.6 812.4 61.2 86.9 12.9 9.1 0.7

F

3 582.2 1 088.3 415.6 63.8 38.9 11.8 4.6 0.7

M

4 394.2 1 283.0 396.9 58.7 47.9 14.0 4.5 0.7

Peru Both 75 <5 20 398.0 672.6 5,818.1 101.1 222.4 7.3 69.2 1.2

F

9 150.4 616.4 2 798.7 99.3 99.8 6.7 33.3 1.2

M

11 247.6 726.6 3 019.4 102.8 122.7 7.9 36.0 1.2

Philippines Both <5 57 455 244.2 3 948.2 65 457.6 304.5 4 989.2 43.3 788.6 3.7

134

F

210 002.3 3 751.0 31 886.4 304.9 2 302.4 41.1 383.2 3.7

M

245 242.0 4 134.3 33 571.1 304.1 2 686.8 45.3 405.4 3.7

Poland Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Portugal Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Qatar Both >95 <5 16.9 13.0 2.6 1.2 0.2 0.1 0.0 0.0

F

6.6 10.4 1.2 1.0 0.1 0.1 0.0 0.0

M

10.3 15.6 1.5 1.3 0.1 0.2 0.0 0.0

Republic of Korea Both >95 <5 119.3 5.4 29.0 0.6 1.2 0.1 0.2 0.0

F

59.0 5.5 14.0 0.6 0.6 0.1 0.1 0.0

M

60.3 5.2 15.0 0.6 0.6 0.1 0.1 0.0

Republic of

Moldova

Both 92 8 957.3 439.4 60.7 14.4 10.4 4.8 0.7 0.2

F

409.9 390.2 29.5 14.5 4.5 4.2 0.3 0.2

M

547.4 485.3 31.2 14.4 6.0 5.3 0.4 0.2

Romania Both 86 <5 6 372.2 674.9 630.6 30.3 69.4 7.4 7.3 0.4

F

2 865.7 623.7 350.1 34.5 31.2 6.8 4.1 0.4

M

3 506.5 723.4 280.6 26.2 38.2 7.9 3.3 0.3

Russian Federation Both >95 <5 2 056.0 21.5 340.2 2.2 22.1 0.2 3.6 0.0

F

942.0 20.3 184.9 2.5 10.1 0.2 2.0 0.0

M

1 114.0 22.7 155.4 2.0 12.0 0.2 1.6 0.0

Rwanda Both <5 >95 93 389.2 5 367.9 17 300.2 561.1 1 023.4 58.8 207.6 6.7

F

42 201.2 4 870.1 8 381.9 542.1 462.3 53.4 100.5 6.5

M

51 188.0 5 862.0 8 918.3 580.2 561.0 64.2 107.1 7.0

Saint Kitts and

Nevis

Both >95 <5 NA NA NA NA NA NA NA NA

135

F


M


Saint Lucia Both >95 <5 6.9 63.1 0.3 1.1 0.1 0.7 0.0 0.0

F

3.4 62.0 0.2 1.9 0.0 0.7 0.0 0.0

M

3.6 64.2 0.0 0.3 0.0 0.7 0.0 0.0

Saint Vincent and

the Grenadines

Both >95 <5 14.3 172.7 1.1 5.9 0.2 1.9 0.0 0.1

F

6.9 169.3 0.6 6.5 0.1 1.8 0.0 0.1

M

7.4 176.1 0.5 5.3 0.1 1.9 0.0 0.1

Samoa Both <5 68 311.1 1 324.6 43.0 88.9 3.4 14.4 0.5 1.0

F

126.2 1 113.8 18.0 77.1 1.4 12.1 0.2 0.9

M

184.9 1 521.0 25.0 99.8 2.0 16.5 0.3 1.2

San Marino Both >95 <5 NA NA NA NA NA NA NA NA

F


M


Sao Tome and

Principe

Both <5 83 1 362.7 4 349.7 300.2 545.9 14.9 47.6 3.6 6.5

F

564.0 3 632.4 151.3 554.3 6.2 39.7 1.8 6.6

M

798.7 5 054.6 148.9 537.7 8.7 55.3 1.8 6.4

Saudi Arabia Both >95 <5 1 917.2 64.6 216.4 4.1 21.0 0.7 2.4 0.0

F

938.7 64.2 127.9 4.9 10.3 0.7 1.4 0.1

M

978.6 65.0 88.4 3.3 10.7 0.7 1.0 0.0

Senegal Both <5 68 161 812.9 6 359.8 20 185.9 494.5 1 776.0 69.8 242.4 5.9

F

73 518.8 5 859.1 10 889.6 539.8 807.5 64.4 130.5 6.5

M

88 294.1 6 846.9 9 296.2 450.2 968.6 75.1 111.9 5.4

Serbia Both 76 <5 417.4 89.0 53.2 5.4 4.4 0.9 0.5 0.1

F

186.2 81.3 24.1 5.0 2.0 0.9 0.2 0.0

M

231.1 96.3 29.1 5.7 2.5 1.0 0.3 0.1

Seychelles Both 90 <5 11.3 143.7 5.4 41.8 0.1 1.6 0.1 0.5

136

F

4.5 118.5 2.6 40.8 0.0 1.3 0.0 0.5

M

6.7 167.9 2.8 42.8 0.1 1.8 0.0 0.5

Sierra Leone Both <5 >95 203 840.7 17 867.4 22 848.1 1 144.6 2 232.3 195.7 272.1 13.6

F

98 725.7 17 362.6 12 599.0 1 258.9 1 081.2 190.1 150.0 15.0

M

105 115.1 18 369.1 10 249.0 1 029.6 1 151.0 201.1 122.2 12.3

Singapore Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Slovakia Both >95 <5 84.8 30.1 16.3 3.0 0.9 0.3 0.2 0.0

F

38.5 28.0 9.5 3.5 0.4 0.3 0.1 0.0

M

46.3 32.2 6.8 2.4 0.5 0.3 0.1 0.0

Slovenia Both >95 <5 3.9 3.6 2.0 1.0 0.0 0.0 0.0 0.0

F

1.9 3.7 0.3 0.3 0.0 0.0 0.0 0.0

M

1.9 3.5 1.7 1.6 0.0 0.0 0.0 0.0

Solomon Islands Both 8 92 3 880.4 4 689.4 200.8 131.9 42.4 51.2 2.3 1.5

F

1 901.6 4 743.8 87.0 118.1 20.8 51.8 1.0 1.4

M

1 978.8 4 638.4 113.7 144.8 21.6 50.7 1.3 1.7

Somalia Both <5 >95 895 779.6 34 228.7 52 304.9 1 292.6 9 866.4 377.0 623.9 15.4

F

432 336.2 33 319.0 28 148.6 1 397.7 4 762.6 367.0 335.6 16.7

M

463 443.4 35 123.4 24 156.4 1 188.5 5 103.7 386.8 288.4 14.2

South Africa Both 85 <5 126 567.7 2 218.6 8 149.6 76.6 1 383.1 24.2 97.4 0.9

F

59 177.1 2 096.4 3 836.4 72.7 646.7 22.9 45.6 0.9

M

67 390.7 2 338.3 4 313.1 80.5 736.4 25.6 51.7 1.0

South Sudan Both <5 >95 403 214.0 20 945.7 17 876.2 559.1 4 442.2 230.8 213.3 6.7

F

194 474.8 20 508.5 9 406.2 595.5 2 143.0 226.0 112.2 7.1

M

208 739.3 21 370.0 8 470.0 523.6 2 299.3 235.4 101.1 6.3

Spain Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

137

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Sri Lanka Both <5 74 7 689.0 480.1 2 436.8 70.6 83.0 5.2 28.1 0.8

F

3 182.3 404.0 1 303.7 75.9 34.4 4.4 15.1 0.9

M

4 506.7 553.7 1 133.1 65.5 48.6 6.0 13.0 0.7

Sudan Both <5 59 476 142.0 8 015.4 20 232.5 195.4 5 254.6 88.5 239.5 2.3

F

270 676.2 9 270.5 10 540.7 206.8 2 987.3 102.3 124.9 2.5

M

205 465.8 6 802.2 9 691.8 184.4 2 267.2 75.1 114.6 2.2

Suriname Both 90 <5 146.9 291.9 14.7 14.9 1.6 3.2 0.2 0.2

F

63.0 259.4 7.3 15.4 0.7 2.8 0.1 0.2

M

83.9 322.3 7.4 14.5 0.9 3.5 0.1 0.2

Sweden Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Switzerland Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Syrian Arab

Republic

Both >95 <5 733.6 34.9 155.3 3.2 8.0 0.4 1.8 0.0

F

326.8 32.0 92.9 4.0 3.6 0.4 1.1 0.0

M

406.9 37.8 62.4 2.5 4.5 0.4 0.7 0.0

Tajikistan Both 80 <5 34 122.4 2 884.4 2 555.6 135.3 372.8 31.5 29.8 1.6

F

16 094.5 2 794.5 1 350.2 146.9 175.9 30.5 15.7 1.7

M

18 027.9 2 969.7 1 205.4 124.3 196.9 32.4 14.0 1.4

Thailand Both 74 <5 15 863.0 421.0 2 744.5 32.7 172.7 4.6 32.1 0.4

F

6 696.7 365.5 977.7 24.0 73.0 4.0 11.2 0.3

M

9 166.3 473.6 1 766.8 41.0 99.8 5.2 21.0 0.5

The former

Yugoslav Republic

of Macedonia

Both 66 <5 534.5 453.0 32.0 13.8 5.8 4.9 0.3 0.1

F

256.9 447.3 3.9 3.5 2.8 4.8 0.0 0.0

138

M

277.6 458.3 28.1 23.7 3.0 4.9 0.3 0.2

Timor-Leste Both 7 93 23 025.5 11 172.3 917.4 262.9 251.9 122.2 10.9 3.1

F

11 795.4 11 678.5 521.4 305.0 129.1 127.8 6.2 3.6

M

11 230.1 10 685.8 395.9 222.5 122.8 116.9 4.7 2.6

Togo Both 7 93 142 733.2 12 138.6 19 804.5 987.5 1 568.6 133.4 238.1 11.9

F

62 593.9 10 678.3 11 690.4 1 169.1 688.1 117.4 140.4 14.0

M

80 139.3 13 590.1 8 114.1 806.9 880.4 149.3 97.6 9.7

Tonga Both 59 <5 136.4 1 072.9 17.6 67.2 1.5 11.6 0.2 0.8

F

77.8 1 255.9 7.3 58.1 0.8 13.6 0.1 0.7

M

58.6 898.9 10.3 75.6 0.6 9.8 0.1 0.9

Trinidad and

Tobago

Both >95 <5 22.9 24.2 1.2 0.7 0.2 0.3 0.0 0.0

F

10.6 22.7 0.4 0.4 0.1 0.2 0.0 0.0

M

12.4 25.7 0.9 0.9 0.1 0.3 0.0 0.0

Tunisia Both >95 <5 243.2 23.1 30.2 1.8 2.7 0.3 0.3 0.0

F

124.8 24.3 19.2 2.3 1.4 0.3 0.2 0.0

M

118.4 22.0 11.0 1.3 1.3 0.2 0.1 0.0

Turkey Both NA


F


M


Turkmenistan Both >95 <5 1 003.4 141.5 31.8 3.1 11.0 1.5 0.4 0.0

F

427.0 122.2 14.9 2.9 4.7 1.3 0.2 0.0

M

576.5 160.1 16.9 3.2 6.3 1.8 0.2 0.0

Tuvalu Both 50 50 NA NA NA NA NA NA NA NA

F


M


Uganda Both <5 >95 735 637.6 9 555.1 97 865.2 801.5 8 078.3 104.9 1 174.7 9.6

F

339 898.6 8 913.8 49 416.8 815.4 3 730.2 97.8 593.0 9.8

M

395 738.9 10 184.4 48 448.4 787.9 4 348.1 111.9 581.7 9.5

139

Ukraine Both >95 <5 1 408.3 60.3 125.4 2.8 15.2 0.7 1.2 0.0

F

750.0 66.3 69.0 3.2 8.1 0.7 0.7 0.0

M

658.4 54.7 56.4 2.5 7.1 0.6 0.5 0.0

United Arab

Emirates

Both >95 <5 53.1 11.4 3.8 0.5 0.6 0.1 0.0 0.0

F

29.8 13.1 1.4 0.3 0.3 0.1 0.0 0.0

M

23.2 9.8 2.4 0.6 0.2 0.1 0.0 0.0

United Kingdom Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

United Republic of

Tanzania

Both <5 >95 885 108.1 9 166.9 96 415.9 626.2 9 719.6 100.7 1 148.4 7.5

F

432 226.7 9 038.0 50 695.0 660.5 4 746.4 99.2 603.5 7.9

M

452 881.4 9 293.4 45 720.9 592.1 4 973.2 102.1 544.9 7.1

United States of

America

Both >95 <5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

F

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Uruguay Both >95 <5 41.5 17.3 3.2 0.6 0.4 0.2 0.0 0.0

F

18.1 15.4 1.0 0.4 0.2 0.2 0.0 0.0

M

23.4 19.1 2.2 0.9 0.3 0.2 0.0 0.0

Uzbekistan Both 92 8 16 830.9 528.6 2 626.3 46.8 183.6 5.8 31.1 0.6

F

7 542.3 491.5 1 324.3 48.4 82.3 5.4 15.7 0.6

M

9 288.6 563.1 1 302.1 45.2 101.3 6.1 15.4 0.5

Vanuatu Both <5 87 1 297.3 3 768.9 94.8 149.9 14.1 41.1 1.1 1.7

F

610.8 3 674.3 36.1 119.6 6.7 40.1 0.4 1.4

M

686.5 3 857.3 58.7 177.6 7.5 42.1 0.7 2.1

Venezuela

(Bolivarian

Republic of)

Both >95 <5 2 899.1 97.5 195.5 3.4 31.7 1.1 2.3 0.0

F

1 257.1 86.5 91.0 3.2 13.7 0.9 1.1 0.0

140

M

1 642.1 108.0 104.5 3.5 17.9 1.2 1.3 0.0

Viet Nam Both 67 <5 113 253.8 1 459.3 3 228.0 23.0 1 237.5 15.9 36.1 0.3

F

44 466.4 1 210.3 1 146.8 17.0 485.7 13.2 12.3 0.2

M

68 787.5 1 683.2 2 081.2 28.4 751.8 18.4 23.9 0.3

Yemen Both 65 <5 224 534.9 5 509.8 6 560.1 93.3 2 462.5 60.4 76.8 1.1

F

127 856.2 6 414.6 3 643.3 105.8 1 402.5 70.4 42.8 1.2

M

96 678.7 4 643.6 2 916.8 81.3 1 060.1 50.9 34.0 0.9

Zambia Both <5 84 258 792.2 9 176.6 24 151.9 518.1 2 840.8 100.7 288.7 6.2

F

123 126.0 8 813.7 11 972.1 516.8 1 352.0 96.8 143.0 6.2

M

135 666.2 9 532.9 12 179.8 519.5 1 488.8 104.6 145.7 6.2

Zimbabwe Both <5 71 188 041.6 7 404.9 24 163.5 583.2 2 064.8 81.3 289.8 7.0

F

94 904.9 7 511.4 13 445.1 650.6 1 042.4 82.5 161.3 7.8

M

93 136.7 7 299.5 10 718.4 516.1 1 022.4 80.1 128.5 6.2

B, both sexes; F, females; M, males

Table 6. Joint effects of exposure of children to ambient and household PM2.5 and burden of disease, by country, 2016

Country Sex No. of DALYs

(< 5 years)

DALYs rate

per 100 000

No. of

DALYs (5 -14

DALYs rate

per 100 000

No. of deaths

(<5 years)

Death rate

per 100 000

No. of

deaths (5 -14

Death rate

per 100 000

141

(< 5 years) years) (5 -14 years) (<5 years) years) (5 -14 years)

Afghanistan Both 743 587.6 14 210.0 53 573.7 537.5 8 177.9 156.3 636.7 6.4

F 388 748.0 15 280.0 31 906.4 657.0 4 279.1 168.2 379.7 7.8

M 354 839.6 13 197.5 21 667.3 424.0 3 898.8 145.0 257.0 5.0

Albania Both 1 532.4 863.9 251.5 73.7 16.5 9.3 2.9 0.8

F 726.6 847.8 182.3 111.5 7.8 9.1 2.1 1.3

M 805.7 878.9 69.1 38.9 8.7 9.5 0.7 0.4

Algeria Both 91 680.0 1 951.0 4 110.7 58.1 1 003.8 21.4 47.6 0.7

F 53 967.9 2 346.9 2 167.2 62.5 591.2 25.7 25.1 0.7

M 37 712.1 1 571.6 1 943.5 53.8 412.6 17.2 22.5 0.6

Andorra Both NA NA NA NA NA NA NA NA

F NA NA NA NA NA NA NA NA

M NA NA NA NA NA NA NA NA

Angola Both 824 454.4 15 623.2 39 623.8 480.1 9 071.4 171.9 472.5 5.7

F 432 088.5 16 446.7 20 416.8 490.0 4 758.3 181.1 243.3 5.8

M 392 365.8 14 806.8 19 206.9 469.9 4 313.2 162.8 229.2 5.6

Antigua and

Barbuda

Both 5.2 64.4 3.2 19.5 0.1 0.7 0.0 0.2

F 2.0 50.1 0.1 1.3 0.0 0.5 0.0 0.0

M 3.2 78.5 3.1 37.6 0.0 0.8 0.0 0.4

Argentina Both 6 806.6 182.2 707.2 9.8 74.0 2.0 8.2 0.1

F 3 096.2 168.7 370.7 10.4 33.7 1.8 4.3 0.1

M 3 710.4 195.2 336.5 9.1 40.4 2.1 3.9 0.1

Armenia Both 1 319.8 652.7 171.1 45.0 14.3 7.1 1.9 0.5

F 610.4 644.3 78.9 44.6 6.6 7.0 0.9 0.5

M 709.4 660.1 92.2 45.3 7.7 7.2 1.1 0.5

Australia Both 228.6 14.7 47.2 1.6 2.4 0.2 0.5 0.0

F 100.6 13.3 33.4 2.3 1.1 0.1 0.4 0.0

M 128.0 16.1 13.7 0.9 1.3 0.2 0.1 0.0

142

Austria Both 15.4 3.8 36.0 4.4 0.2 0.0 0.4 0.1

F 7.4 3.7 35.3 8.9 0.1 0.0 0.4 0.1

M 8.1 3.8 0.7 0.2 0.1 0.0 0.0 0.0

Azerbaijan Both 12 697.7 1 425.9 1 152.9 84.6 138.4 15.5 13.5 1.0

F 6 528.9 1 575.9 528.8 83.7 71.2 17.2 6.2 1.0

M 6 168.9 1 295.4 624.1 85.4 67.3 14.1 7.3 1.0

Bahamas Both 124.4 451.8 7.3 13.8 1.4 4.9 0.1 0.2

F 59.8 446.5 3.1 11.9 0.6 4.8 0.0 0.1

M 64.6 456.9 4.2 15.6 0.7 5.0 0.0 0.2

Bahrain Both 110.7 103.6 79.8 43.7 1.2 1.1 0.9 0.5

F 56.2 108.2 44.2 49.3 0.6 1.1 0.5 0.6

M 54.5 99.4 35.6 38.3 0.6 1.0 0.4 0.4

Bangladesh Both 1 049 873.4 6 890.9 44 378.0 139.3 11 487.3 75.4 522.6 1.6

F 471 209.8 6 322.6 26 686.1 171.2 5 156.7 69.2 315.8 2.0

M 578 663.6 7 435.2 17 691.9 108.7 6 330.6 81.3 206.8 1.3

Barbados Both 19.4 112.3 10.2 27.2 0.2 1.2 0.1 0.3

F 9.2 108.1 1.7 9.2 0.1 1.1 0.0 0.1

M 10.2 116.4 8.5 44.3 0.1 1.2 0.1 0.5

Belarus Both 461.0 79.6 78.1 8.0 4.8 0.8 0.6 0.1

F 203.5 72.4 37.8 7.9 2.1 0.8 0.3 0.1

M 257.6 86.3 40.3 8.0 2.7 0.9 0.3 0.1

Belgium Both 147.6 23.0 22.0 1.7 1.6 0.2 0.2 0.0

F 62.8 20.0 13.3 2.1 0.7 0.2 0.1 0.0

M 84.8 25.7 8.6 1.3 0.9 0.3 0.1 0.0

Belize Both 247.4 614.0 29.5 38.4 2.7 6.7 0.3 0.4

F 122.0 611.5 12.3 32.5 1.3 6.6 0.1 0.4

M 125.5 616.4 17.2 44.1 1.4 6.7 0.2 0.5

Benin Both 348 671.9 19 638.6 35 215.4 1 220.6 3 834.5 216.0 421.2 14.6

143

F 183 817.0 21 030.0 18 528.9 1 302.7 2 022.7 231.4 221.5 15.6

M 164 855.0 18 289.2 16 686.5 1 140.8 1 811.8 201.0 199.8 13.7

Bhutan Both 3 407.2 4 880.4 335.0 230.9 37.2 53.3 4.0 2.8

F 1 533.1 4 466.9 188.2 263.5 16.7 48.8 2.3 3.2

M 1 874.1 5 280.3 146.8 199.3 20.5 57.7 1.8 2.4

Bolivia

(Plurinational

State of)

Both 40 128.4 3 376.4 5 121.9 222.8 439.5 37.0 61.4 2.7

F 18 823.5 3 234.0 2 526.0 223.6 206.2 35.4 30.3 2.7

M 21 304.9 3 513.0 2 595.8 221.9 233.3 38.5 31.1 2.7

Bosnia and

Herzegovina

Both 432.5 276.1 45.4 13.2 4.6 2.9 0.4 0.1

F 201.9 265.9 19.2 11.6 2.1 2.8 0.1 0.1

M 230.6 285.6 26.2 14.8 2.4 3.0 0.2 0.1

Botswana Both 8 588.3 3 313.2 872.3 192.8 94.2 36.3 10.4 2.3

F 4 144.1 3 230.3 411.9 183.5 45.4 35.4 4.9 2.2

M 4 444.2 3 394.5 460.4 202.0 48.8 37.2 5.5 2.4

Brazil Both 58 241.8 390.4 5 269.7 17.0 633.2 4.2 60.5 0.2

F 26 914.8 369.4 2 578.1 16.9 292.4 4.0 29.4 0.2

M 31 327.0 410.5 2 691.6 17.0 340.8 4.5 31.1 0.2

Brunei

Darussalam

Both 6.8 19.7 3.8 5.9 0.1 0.2 0.0 0.1

F 3.1 18.4 2.5 8.1 0.0 0.2 0.0 0.1

M 3.7 20.9 1.3 3.8 0.0 0.2 0.0 0.0

Bulgaria Both 1 471.8 454.0 280.1 41.1 16.0 4.9 3.2 0.5

F 658.1 417.9 132.0 39.9 7.1 4.5 1.5 0.5

M 813.7 488.0 148.2 42.3 8.8 5.3 1.7 0.5

Burkina Faso Both 450 882.7 13 999.8 56 272.2 1 073.4 4 957.7 153.9 673.5 12.8

F 213 872.9 13 519.7 31 756.9 1 233.7 2 353.0 148.7 379.9 14.8

M 237 009.8 14 463.2 24 515.4 918.7 2 604.7 159.0 293.6 11.0

144

Burundi Both 303 901.0 15 983.5 53 112.4 1 882.6 3 338.6 175.6 637.5 22.6

F 145 148.7 15 367.7 28 995.1 2 048.5 1 595.2 168.9 347.9 24.6

M 158 752.3 16 591.4 24 117.2 1 715.5 1 743.4 182.2 289.6 20.6

Cabo Verde Both 1 256.2 2 299.4 59.4 53.6 13.7 25.1 0.7 0.6

F 567.6 2 103.0 29.3 53.4 6.2 23.0 0.3 0.6

M 688.6 2 491.3 30.1 53.9 7.5 27.2 0.4 0.6

Cambodia Both 103 146.9 5 856.4 9 325.3 292.6 1 126.1 63.9 109.7 3.4

F 46 771.4 5 397.7 4 176.7 267.1 510.9 59.0 49.0 3.1

M 56 375.6 6 300.6 5 148.7 317.2 615.2 68.8 60.7 3.7

Cameroon Both 641 356.5 16 858.9 118 525.0 1 895.7 7 048.7 185.3 1,418.4 22.7

F 290 059.3 15 403.3 58 157.5 1 874.7 3 189.0 169.4 696.0 22.4

M 351 297.2 18 285.7 60 367.5 1 916.4 3 859.6 200.9 722.4 22.9

Canada Both 157.4 8.2 28.9 0.7 1.7 0.1 0.3 0.0

F 72.7 7.7 16.9 0.9 0.8 0.1 0.2 0.0

M 84.7 8.6 12.1 0.6 0.9 0.1 0.1 0.0

Central African

Republic

Both 213 754.1 29 263.2 9 978.9 786.8 2 347.2 321.3 119.2 9.4

F 109 229.5 29 952.5 5 358.5 839.4 1 200.4 329.2 64.0 10.0

M 104 524.6 28 576.0 4 620.3 733.5 1 146.9 313.5 55.2 8.8

Chad Both 1 177 331.0 44 154.5 61 071.6 1 460.4 12 982.7 486.9 733.7 17.5

F 566 691.8 42 880.3 33 786.8 1 628.5 6 251.9 473.1 406.1 19.6

M 610 639.2 45 406.7 27 284.8 1 294.9 6 730.8 500.5 327.6 15.5

Chile Both 1 499.5 126.7 115.9 4.6 16.2 1.4 1.2 0.0

F 672.8 115.9 57.8 4.7 7.3 1.3 0.6 0.0

M 826.7 137.1 58.1 4.6 9.0 1.5 0.6 0.0

China Both 1 046 128.8 1 213.9 79 016.5 48.4 11 377.2 13.2 874.8 0.5

F 462 779.3 1 156.8 45 380.3 60.2 5 016.3 12.5 484.7 0.6

M 583 349.5 1 263.4 33 636.2 38.3 6 360.9 13.8 390.1 0.4

Colombia Both 23 646.1 637.0 2 382.2 30.1 258.3 7.0 28.4 0.4

145

F 10 993.8 605.3 1 063.5 27.5 120.1 6.6 12.6 0.3

M 12 652.3 667.4 1 318.7 32.7 138.2 7.3 15.8 0.4

Comoros Both 19 120.9 16 048.4 1 043.8 524.7 210.0 176.3 12.4 6.3

F 9 376.7 16 043.6 522.4 534.5 103.0 176.3 6.2 6.4

M 9 744.2 16 053.1 521.4 515.3 107.0 176.3 6.2 6.2

Congo Both 76 625.6 9 296.3 5 512.9 408.9 841.1 102.0 66.2 4.9

F 37 555.9 9 199.9 3 189.4 475.6 412.7 101.1 38.3 5.7

M 39 069.7 9 391.0 2 323.5 342.9 428.5 103.0 27.9 4.1

Cook Islands Both NA NA NA NA NA NA NA NA



Costa Rica Both 589.1 170.4 75.0 10.4 6.3 1.8 0.8 0.1

F 274.9 162.9 41.6 11.8 2.9 1.7 0.4 0.1

M 314.2 177.6 33.3 9.0 3.4 1.9 0.4 0.1

Côte d'Ivoire Both 738 191.4 19 121.1 107 634.4 1 728.2 8 107.7 210.0 1 293.4 20.8

F 326 954.3 17 053.9 61 198.6 1 968.2 3 592.5 187.4 735.3 23.6

M 411 237.1 21 160.3 46 435.8 1488.9 4 515.2 232.3 558.1 17.9

Croatia Both 151.8 77.3 20.8 4.9 1.6 0.8 0.2 0.0

F 62.4 65.4 1.5 0.7 0.7 0.7 0.0 0.0

M 89.4 88.6 19.3 8.9 1.0 0.9 0.2 0.1

Cuba Both 2 327.2 365.7 173.8 14.2 25.3 4.0 1.9 0.2

F 1 070.7 345.9 111.0 18.7 11.6 3.8 1.2 0.2

M 1 256.4 384.4 62.8 10.0 13.7 4.2 0.6 0.1

Cyprus Both 5.7 8.7 0.2 0.1 0.1 0.1 0.0 0.0

F 2.7 8.6 0.1 0.1 0.0 0.1 0.0 0.0

M 3.0 8.8 0.1 0.1 0.0 0.1 0.0 0.0

Czechia Both 345.7 64.7 102.9 9.5 3.7 0.7 1.1 0.1

F 142.3 54.8 38.5 7.3 1.5 0.6 0.4 0.1

146

M 203.4 74.1 64.4 11.6 2.2 0.8 0.7 0.1

Democratic

People's Republic

of Korea

Both 62 342.0 3 611.1 4 152.2 116.4 680.4 39.4 47.8 1.3

F 27 485.5 3 260.0 2 354.5 134.8 299.5 35.5 27.0 1.5

M 34 856.5 3 946.2 1 797.7 98.7 380.9 43.1 20.8 1.1

Democratic

Republic of the

Congo

Both 2 977 828.2 20 545.2 227 190.6 1 032.8 32 647.3 225.2 2 736.3 12.4

F 1 483 995.6 20 681.3 128 347.4 1 175.4 16 275.6 226.8 1 545.2 14.2

M 1 493 832.5 20 411.8 98 843.2 892.3 16 371.7 223.7 1 191.1 10.8

Denmark Both 35.6 12.5 3.9 0.6 0.4 0.1 0.0 0.0

F 16.7 12.0 3.3 1.0 0.2 0.1 0.0 0.0

M 19.0 13.0 0.6 0.2 0.2 0.1 0.0 0.0

Djibouti Both 11 510.3 11 253.5 2 632.0 1 348.4 125.9 123.1 31.7 16.2

F 5 313.0 10 536.2 1 314.8 1 363.6 58.1 115.2 15.8 16.4

M 6 197.3 11 950.9 1 317.1 1 333.5 67.8 130.8 15.9 16.1

Dominica Both NA NA NA NA NA NA NA NA



Dominican

Republic

Both 15 211.5 1 435.7 799.6 38.1 166.6 15.7 9.2 0.4

F 6 061.5 1 168.2 393.1 38.2 66.4 12.8 4.5 0.4

M 9 150.0 1 692.3 406.6 38.1 100.3 18.5 4.7 0.4

Ecuador Both 14 450.1 896.9 1 148.3 37.1 158.1 9.8 13.7 0.4

F 6 682.0 849.1 553.7 36.6 73.1 9.3 6.6 0.4

M 7 768.1 942.5 594.6 37.6 85.0 10.3 7.1 0.4

Egypt Both 231 920.0 1 801.2 58 961.3 308.2 2,533.9 19.7 702.6 3.7

F 101 414.2 1 625.7 26 362.2 284.0 1 108.4 17.8 313.2 3.4

M 130 505.8 1 966.2 32 599.1 330.9 1 425.5 21.5 389.3 4.0

147

El Salvador Both 5 853.9 1 015.4 810.5 68.3 63.9 11.1 9.9 0.8

F 2,279.7 809.8 302.5 52.1 24.8 8.8 3.6 0.6

M 3,574.2 1,211.7 508.0 83.9 39.0 13.2 6.2 1.0

Equatorial

Guinea

Both 30 835.4 16 976.2 2 858.1 1 040.8 338.1 186.2 34.2 12.5

F 13 223.4 14 750.4 1 613.3 1 190.7 145.1 161.9 19.3 14.2

M 17 612.0 19 145.3 1 244.8 894.9 193.0 209.8 14.9 10.7

Eritrea Both 85 692.6 11 521.8 10 633.4 793.5 940.9 126.5 126.8 9.5

F 39 625.4 10 883.1 5 030.6 765.7 435.1 119.5 59.9 9.1

M 46 067.3 12 134.4 5 602.8 820.3 505.8 133.2 66.9 9.8

Estonia Both 21.9 32.4 13.3 9.2 0.2 0.3 0.1 0.1

F 10.3 31.1 5.4 7.7 0.1 0.3 0.1 0.1

M 11.6 33.6 7.9 10.6 0.1 0.3 0.1 0.1

eSwatini Both 18 542.3 10 323.9 1 796.5 556.8 203.1 113.1 21.4 6.6

F 8 647.9 9 693.2 967.2 601.7 94.7 106.2 11.5 7.2

M 9 894.4 10 946.4 829.3 512.3 108.4 119.9 9.9 6.1

Ethiopia Both 1 849 426.1 12 185.6 261 665.8 972.3 20 330.0 134.0 3 140.1 11.7

F 824 558.8 11 028.5 127 071.8 954.6 9 063.9 121.2 1 523.8 11.4

M 1 024 867.4 13 309.0 134 594.1 989.7 11 266.1 146.3 1 616.2 11.9

Fiji Both 2 058.4 2 377.5 264.9 155.5 22.4 25.9 3.1 1.8

F 996.6 2 367.2 141.2 171.3 10.9 25.8 1.7 2.0

M 1 061.8 2 387.2 123.8 140.6 11.6 26.0 1.5 1.7

Finland Both 11.4 3.9 0.4 0.1 0.1 0.0 0.0 0.0

F 5.4 3.7 0.2 0.1 0.1 0.0 0.0 0.0

M 6.0 4.0 0.3 0.1 0.1 0.0 0.0 0.0

France Both 354.4 9.2 95.0 1.2 3.6 0.1 0.7 0.0

F 169.4 9.0 44.1 1.1 1.7 0.1 0.3 0.0

M 185.0 9.4 50.8 1.3 1.9 0.1 0.4 0.0

Gabon Both 14 354.1 5 246.1 2 008.6 460.3 157.5 57.6 24.6 5.6

148

F 6 520.0 4 818.9 990.8 458.1 71.6 52.9 12.1 5.6

M 7 834.1 5 664.1 1 017.7 462.4 85.9 62.1 12.5 5.7

Gambia Both 43 944.2 12 190.4 3 936.4 693.4 483.8 134.2 47.0 8.3

F 20 850.8 11 684.3 2 326.7 826.7 229.7 128.7 27.8 9.9

M 23 093.4 12 686.6 1 609.7 562.4 254.1 139.6 19.2 6.7

Georgia Both 1 287.8 474.8 448.2 94.8 14.0 5.2 5.3 1.1

F 641.4 491.1 172.3 77.2 7.0 5.3 2.0 0.9

M 646.4 459.7 275.9 110.6 7.0 5.0 3.3 1.3

Germany Both 487.6 13.7 137.5 1.9 5.2 0.1 1.5 0.0

F 221.7 12.8 51.1 1.5 2.4 0.1 0.5 0.0

M 265.9 14.6 86.4 2.4 2.8 0.2 1.0 0.0

Ghana Both 339 033.1 8 298.9 34 463.0 505.0 3 724.0 91.2 413.0 6.1

F 150 795.4 7 538.8 18 151.8 544.0 1 656.1 82.8 217.5 6.5

M 188 237.7 9 028.1 16 311.2 467.7 2 067.9 99.2 195.5 5.6

Greece Both 282.4 59.6 43.2 3.8 3.1 0.6 0.5 0.0

F 128.6 56.1 20.0 3.6 1.4 0.6 0.2 0.0

M 153.8 62.9 23.2 4.0 1.7 0.7 0.3 0.0

Grenada Both 75.9 768.8 2.5 13.4 0.8 8.4 0.0 0.1

F 35.7 741.8 0.2 2.0 0.4 8.1 0.0 0.0

M 40.2 794.4 2.3 24.3 0.4 8.7 0.0 0.3

Guatemala Both 91 132.4 4 505.6 6 784.8 174.4 997.0 49.3 81.0 2.1

F 42 233.7 4 270.1 3 484.7 183.0 462.1 46.7 41.6 2.2

M 48 898.7 4 730.8 3 300.0 166.1 535.0 51.8 39.5 2.0

Guinea Both 390 832.7 19 710.2 42 456.6 1 293.9 4 294.4 216.6 508.3 15.5

F 185 068.8 18 786.1 26 421.0 1 621.8 2 034.3 206.5 316.1 19.4

M 205 764.0 20 622.7 16 035.6 970.7 2 260.1 226.5 192.2 11.6

Guinea-Bissau Both 60 451.1 20 785.1 3 357.5 722.4 666.6 229.2 40.2 8.6

F 28 723.7 19 839.1 1 799.9 774.9 316.7 218.8 21.5 9.3

149

M 31 727.4 21 722.9 1 557.6 670.0 349.9 239.6 18.7 8.0

Guyana Both 1 465.2 1 917.4 251.1 166.2 16.0 21.0 3.0 2.0

F 679.2 1 823.7 113.1 153.8 7.4 19.9 1.3 1.8

M 786.1 2 006.5 138.0 177.9 8.6 21.9 1.7 2.2

Haiti Both 211 679.7 17 161.9 15 676.2 657.8 2 323.7 188.4 187.6 7.9

F 95 403.3 15 778.3 6 873.8 586.1 1 047.4 173.2 82.3 7.0

M 116 276.4 18 492.3 8 802.4 727.3 1 276.3 203.0 105.3 8.7

Honduras Both 18 714.4 1 967.4 735.0 36.9 204.5 21.5 8.5 0.4

F 8 370.1 1 795.6 218.0 22.3 91.4 19.6 2.4 0.2

M 10 344.3 2 132.4 517.0 51.0 113.1 23.3 6.1 0.6

Hungary Both 303.6 69.6 12.8 1.3 3.2 0.7 0.0 0.0

F 127.2 60.2 6.1 1.3 1.3 0.6 0.0 0.0

M 176.4 78.6 6.7 1.4 1.9 0.8 0.0 0.0

Iceland Both 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

F 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

M 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0

India Both 9 280 411.0 7 733.8 612 021.7 241.6 101 788.2 84.8 7 234.4 2.9

F 5 000 735.5 8 796.4 346 992.9 289.8 54 893.0 96.6 4 125.3 3.4

M 4 279 675.5 6 777.2 265 028.8 198.3 46 895.2 74.3 3 109.1 2.3

Indonesia Both 804 671.2 3 241.8 47 788.0 100.9 8 784.9 35.4 556.7 1.2

F 395 001.1 3 257.2 23 055.2 99.7 4 314.6 35.6 267.4 1.2

M 409 670.1 3 227.1 24 732.8 102.0 4 470.3 35.2 289.4 1.2

Iran (Islamic

Republic of)

Both 67 188.0 984.8 2 841.3 23.3 733.1 10.7 31.3 0.3

F 35 538.3 1 064.4 1 413.9 23.7 388.3 11.6 15.5 0.3

M 31 649.7 908.5 1 427.4 23.0 344.8 9.9 15.8 0.3

Iraq Both 191 318.0 3 334.5 3 870.2 41.4 2 093.2 36.5 43.7 0.5

F 62 496.4 2 241.0 2 103.6 46.4 684.7 24.6 23.9 0.5

M 128 821.5 4 368.6 1 766.5 36.8 1 408.5 47.8 19.8 0.4

150

Ireland Both 31.3 9.1 3.4 0.5 0.3 0.1 0.0 0.0

F 13.5 8.1 2.7 0.8 0.1 0.1 0.0 0.0

M 17.8 10.1 0.7 0.2 0.2 0.1 0.0 0.0

Israel Both 148.5 17.7 58.7 4.1 1.6 0.2 0.7 0.0

F 72.5 17.7 25.5 3.6 0.8 0.2 0.3 0.0

M 76.0 17.6 33.3 4.5 0.8 0.2 0.4 0.1

Italy Both 279.8 11.3 63.8 1.1 3.0 0.1 0.7 0.0

F 125.5 10.4 43.0 1.6 1.3 0.1 0.5 0.0

M 154.2 12.1 20.8 0.7 1.6 0.1 0.2 0.0

Jamaica Both 551.7 269.0 83.6 18.3 5.9 2.9 0.9 0.2

F 251.0 252.4 42.7 19.1 2.7 2.7 0.5 0.2

M 300.7 284.7 40.9 17.6 3.2 3.1 0.5 0.2

Japan Both 1 882.6 35.2 527.3 4.7 20.2 0.4 5.4 0.0

F 885.3 34.1 216.7 4.0 9.5 0.4 2.2 0.0

M 997.2 36.3 310.6 5.4 10.7 0.4 3.2 0.1

Jordan Both 8 873.6 723.1 694.6 32.2 96.6 7.9 8.0 0.4

F 4 252.8 708.1 402.3 37.8 46.4 7.7 4.7 0.4

M 4 620.8 737.5 292.3 26.8 50.3 8.0 3.3 0.3

Kazakhstan Both 8 157.7 408.5 553.0 18.9 88.8 4.4 6.3 0.2

F 3 674.5 378.4 271.9 19.1 40.0 4.1 3.1 0.2

M 4 483.2 436.9 281.2 18.7 48.8 4.8 3.2 0.2

Kenya Both 607 742.3 8 653.2 38 646.6 302.2 6 671.7 95.0 463.1 3.6

F 294 585.2 8 476.2 15 724.9 247.8 3 234.9 93.1 188.0 3.0

M 313 157.1 8 826.5 22 921.6 355.7 3 436.7 96.9 275.1 4.3

Kiribati Both 1 551.9 10 713.3 88.5 349.1 17.0 117.4 1.0 4.1

F 746.6 10 567.8 26.6 215.0 8.2 115.8 0.3 2.5

M 805.3 10 851.9 61.8 477.4 8.8 119.0 0.7 5.6

Kuwait Both 1 046.9 331.2 219.3 41.1 11.4 3.6 2.5 0.5

151

F 517.5 334.8 99.8 39.0 5.6 3.6 1.1 0.4

M 529.4 327.7 119.5 43.0 5.7 3.6 1.4 0.5

Kyrgyzstan Both 20 013.1 2 631.6 820.5 73.5 218.7 28.8 9.4 0.8

F 9 242.5 2 498.2 372.0 68.1 101.0 27.3 4.2 0.8

M 10 770.6 2 758.0 448.5 78.7 117.7 30.1 5.2 0.9

Lao People's

Democratic

Republic

Both 105 545.5 13 785.8 7 456.0 502.8 1 156.7 151.1 88.2 6.0

F 51 020.7 13 610.3 3 481.1 479.0 559.5 149.3 41.2 5.7

M 54 524.8 13 954.2 3 974.9 525.8 597.2 152.8 47.1 6.2

Latvia Both 93.5 96.6 5.0 2.4 1.0 1.0 0.0 0.0

F 44.6 95.0 2.4 2.4 0.5 1.0 0.0 0.0

M 48.9 98.0 2.6 2.5 0.5 1.0 0.0 0.0

Lebanon Both 937.8 194.1 69.3 7.4 10.1 2.1 0.6 0.1

F 516.4 217.0 37.1 7.9 5.6 2.3 0.3 0.1

M 421.4 171.8 32.1 7.0 4.5 1.9 0.3 0.1

Lesotho Both 44 220.4 15 466.0 3 083.5 620.3 484.2 169.4 36.8 7.4

F 22 325.3 15 726.3 1 761.0 711.9 244.5 172.2 21.1 8.5

M 21 895.0 15 209.4 1 322.6 529.5 239.7 166.5 15.8 6.3

Liberia Both 100 750.4 14 081.4 8 131.9 662.5 1 103.5 154.2 97.9 8.0

F 50 171.1 14 332.2 4 556.3 758.4 549.6 157.0 54.8 9.1

M 50 579.3 13 841.1 3 575.7 570.6 554.0 151.6 43.1 6.9

Libya Both 4 146.2 661.8 735.2 63.3 45.3 7.2 8.4 0.7

F 2 155.4 705.9 354.6 62.6 23.5 7.7 4.0 0.7

M 1 990.8 619.8 380.6 64.0 21.7 6.8 4.4 0.7

Lithuania Both 66.7 43.9 3.9 1.4 0.7 0.5 0.0 0.0

F 34.1 46.0 1.9 1.4 0.4 0.5 0.0 0.0

M 32.6 41.8 2.0 1.4 0.3 0.4 0.0 0.0

Luxembourg Both 1.0 3.1 0.1 0.1 0.0 0.0 0.0 0.0

152

F 0.4 2.8 0.0 0.1 0.0 0.0 0.0 0.0

M 0.5 3.3 0.0 0.1 0.0 0.0 0.0 0.0

Madagascar Both 385 194.1 10 221.2 48 886.6 750.8 4 225.0 112.1 585.2 9.0

F 190 114.3 10 215.2 23 754.0 734.0 2 086.2 112.1 283.8 8.8

M 195 079.8 10 227.0 25 132.6 767.4 2 138.9 112.1 301.4 9.2

Malawi Both 265 781.4 9 138.8 15 383.5 301.6 2 915.0 100.2 183.7 3.6

F 122 858.6 8 546.0 7 070.1 278.8 1 347.4 93.7 84.0 3.3

M 142 922.9 9 718.2 8 313.4 324.1 1 567.6 106.6 99.6 3.9

Malaysia Both 4 161.7 159.3 1 459.2 28.8 45.0 1.7 17.2 0.3

F 1 790.9 142.1 646.2 26.2 19.4 1.5 7.6 0.3

M 2 370.8 175.4 813.1 31.3 25.7 1.9 9.7 0.4

Maldives Both 55.9 143.1 8.1 13.3 0.6 1.6 0.1 0.1

F 30.2 160.9 4.5 15.1 0.3 1.8 0.1 0.2

M 25.7 126.7 3.6 11.6 0.3 1.4 0.0 0.1

Mali Both 665 347.8 19 967.7 44 405.1 841.6 7 319.4 219.7 529.5 10.0

F 338 497.4 20 687.2 25 887.1 997.2 3 725.1 227.7 308.7 11.9

M 326 850.4 19 273.6 18 518.1 691.0 3 594.3 211.9 220.8 8.2

Malta Both 9.1 42.2 0.1 0.1 0.1 0.5 0.0 0.0

F 4.6 43.9 0.0 0.1 0.1 0.5 0.0 0.0

M 4.5 40.5 0.0 0.1 0.0 0.4 0.0 0.0

Marshall Islands Both NA NA NA NA NA NA NA NA



Mauritania Both 95 137.9 14 522.4 4 210.4 393.6 1 046.8 159.8 50.0 4.7

F 39 088.1 12 157.1 2 421.5 459.6 430.1 133.8 28.8 5.5

M 56 049.8 16 802.2 1 788.9 329.5 616.7 184.9 21.2 3.9

Mauritius Both 231.3 339.6 41.4 24.3 2.5 3.7 0.5 0.3

F 98.0 293.5 24.2 29.0 1.1 3.2 0.3 0.3

153

M 133.4 384.0 17.1 19.7 1.5 4.2 0.2 0.2

Mexico Both 97 907.4 845.4 4 141.5 18.0 1 070.0 9.2 49.1 0.2

F 45 281.0 800.1 1 937.1 17.3 494.9 8.7 22.8 0.2

M 52 626.3 888.7 2 204.4 18.8 575.1 9.7 26.3 0.2

Micronesia

(Federated States

of)

Both 644.3 5,532.5 58.1 246.3 7.1 60.6 0.7 2.9

F 295.7 5 251.9 25.2 222.2 3.2 57.5 0.3 2.6

M 348.6 5 795.2 32.8 268.7 3.8 63.4 0.4 3.2

Monaco Both NA NA NA NA NA NA NA NA



Mongolia Both 9 546.4 2 597.0 868.8 167.2 104.0 28.3 10.1 1.9

F 3 438.1 1 895.2 369.9 144.0 37.4 20.6 4.3 1.7

M 6 108.3 3 280.9 499.0 190.0 66.6 35.8 5.8 2.2

Montenegro Both 44.0 121.1 20.7 26.3 0.4 1.2 0.2 0.2

F 19.3 109.9 11.0 29.2 0.2 1.1 0.1 0.3

M 24.6 131.6 9.7 23.7 0.2 1.3 0.1 0.2

Morocco Both 54 513.0 1 553.8 2 318.8 37.3 596.4 17.0 26.7 0.4

F 24 819.8 1 454.2 1 207.4 39.8 271.8 15.9 13.9 0.5

M 29 693.2 1 648.1 1 111.4 34.9 324.6 18.0 12.8 0.4

Mozambique Both 620 295.6 12 531.2 53 519.6 666.6 6 792.8 137.2 644.8 8.0

F 304 753.8 12 407.3 26 973.9 674.0 3 338.0 135.9 324.8 8.1

M 315 541.8 12 653.3 26 545.7 659.3 3 454.8 138.5 320.0 7.9

Myanmar Both 505 643.2 11 142.9 37 658.4 378.9 5 543.8 122.2 447.5 4.5

F 225 973.9 10 029.1 13 890.0 281.2 2 481.4 110.1 164.7 3.3

M 279 669.2 12 241.3 23 768.4 475.5 3 062.4 134.0 282.8 5.7

Namibia Both 26 643.4 7 742.5 2 385.5 418.0 292.5 85.0 28.6 5.0

F 12 658.0 7 405.8 1 107.0 388.6 139.0 81.3 13.3 4.7

154

M 13 985.4 8 074.7 1 278.5 447.3 153.6 88.7 15.4 5.4

Nauru Both NA NA NA NA NA NA NA NA



Nepal Both 190 700.7 6 919.0 12 992.0 202.2 2,086.1 75.7 152.9 2.4

F 84 837.2 6 353.4 6 694.8 213.7 928.0 69.5 79.0 2.5

M 105 863.4 7 450.5 6 297.2 191.3 1,158.0 81.5 73.9 2.2

Netherlands Both 106.7 11.9 72.5 3.8 1.2 0.1 0.8 0.0

F 46.4 10.7 50.6 5.4 0.5 0.1 0.6 0.1

M 60.3 13.1 21.9 2.2 0.7 0.1 0.2 0.0

New Zealand Both 82.5 27.1 7.6 1.2 0.9 0.3 0.1 0.0

F 39.2 26.4 3.5 1.2 0.4 0.3 0.0 0.0

M 43.3 27.8 4.1 1.3 0.5 0.3 0.0 0.0

Nicaragua Both 19 161.4 3 208.2 1 355.7 111.4 209.6 35.1 16.3 1.3

F 7 363.6 2 521.3 666.2 112.8 80.5 27.6 8.0 1.4

M 11,797.8 3,865.5 689.5 110.0 129.1 42.3 8.3 1.3

Niger Both 1 064 295.9 25 233.0 149 607.5 2 426.2 11 740.8 278.4 1 800.6 29.2

F 541 543.6 26 242.9 79 237.6 2 622.4 5 978.1 289.7 953.6 31.6

M 522 752.3 24 265.7 70 369.9 2 237.6 5 762.7 267.5 847.1 26.9

Nigeria Both 8 902 806.0 27 994.9 994 548.2 1 981.3 98 000.8 308.2 11 876.0 23.7

F 4 104 628.2 26 471.5 580 661.6 2 362.6 45 212.4 291.6 6 931.0 28.2

M 4 798,177.0 29 444.5 413 886.6 1 615.5 52 788.4 323.9 4 945.0 19.3

Niue Both NA NA NA NA NA NA NA NA



Norway Both 15.5 5.1 2.6 0.4 0.2 0.1 0.0 0.0

F 6.4 4.3 0.5 0.2 0.1 0.0 0.0 0.0

M 9.1 5.8 2.1 0.7 0.1 0.1 0.0 0.0

155

Oman Both 1 559.4 389.7 149.7 26.1 16.9 4.2 1.6 0.3

F 897.8 461.9 77.3 27.2 9.8 5.0 0.8 0.3

M 661.7 321.5 72.3 25.0 7.1 3.5 0.8 0.3

Pakistan Both 3 489 562.8 13 979.2 165 856.7 390.7 38 252.1 153.2 1 979.6 4.7

F 1 765 736.8 14 711.6 65 119.0 318.8 19 363.8 161.3 774.9 3.8

M 1 723 826.1 13 300.8 100 737.8 457.4 18 888.3 145.7 1 204.7 5.5

Palau Both NA NA NA NA NA NA NA NA



Panama Both 3 354.1 863.6 296.7 40.8 36.6 9.4 3.5 0.5

F 1 456.5 766.3 136.0 38.2 15.9 8.4 1.6 0.5

M 1 897.6 956.9 160.7 43.4 20.7 10.5 1.9 0.5

Papua New

Guinea

Both 98 364.8 9 525.1 6 691.4 352.2 1 075.6 104.2 78.7 4.1

F 42 883.8 8 599.4 2 440.3 265.4 468.8 94.0 28.5 3.1

M 55 481.0 10 389.6 4 251.1 433.6 606.8 113.6 50.1 5.1

Paraguay Both 9 998.8 1 488.7 1 026.5 77.3 108.9 16.2 11.4 0.9

F 4 490.5 1 364.2 515.6 79.2 48.8 14.8 5.7 0.9

M 5 508.3 1 608.4 510.9 75.6 60.1 17.5 5.7 0.8

Peru Both 33 641.7 1 109.3 9 768.1 169.7 366.8 12.1 116.3 2.0

F 15 091.4 1 016.5 4 524.6 160.6 164.5 11.1 53.8 1.9

M 18 550.3 1 198.3 5 243.5 178.5 202.3 13.1 62.5 2.1

Philippines Both 557 310.0 4 833.4 80 775.9 375.8 6 107.8 53.0 973.2 4.5

F 257 084.8 4 592.0 38 670.5 369.8 2 818.7 50.3 464.8 4.4

M 300 225.2 5 061.2 42 105.3 381.5 3 289.2 55.4 508.4 4.6

Poland Both 1 403.7 77.2 595.6 15.5 15.1 0.8 6.9 0.2

F 625.5 70.7 281.8 15.1 6.7 0.8 3.3 0.2

M 778.2 83.3 313.8 15.9 8.4 0.9 3.6 0.2

Portugal Both 95.0 22.0 9.7 1.0 1.0 0.2 0.1 0.0

156

F 43.7 21.0 3.5 0.7 0.5 0.2 0.0 0.0

M 51.3 23.0 6.3 1.2 0.6 0.3 0.1 0.0

Qatar Both 395.1 304.0 64.0 28.2 4.3 3.3 0.7 0.3

F 154.3 242.1 25.6 23.2 1.7 2.6 0.3 0.2

M 240.8 363.7 38.4 33.0 2.6 3.9 0.4 0.4

Republic of

Korea

Both 739.8 33.2 186.5 4.0 7.6 0.3 1.3 0.0

F 366.0 34.1 83.7 3.7 3.8 0.4 0.6 0.0

M 373.8 32.4 102.8 4.2 3.8 0.3 0.7 0.0

Republic of

Moldova

Both 2 470.4 1 134.1 160.9 38.3 26.9 12.4 1.8 0.4

F 1 057.9 1 007.0 74.0 36.3 11.5 11.0 0.8 0.4

M 1 412.6 1 252.5 86.9 40.2 15.4 13.6 1.0 0.5

Romania Both 11 648.5 1 233.7 1 169.0 56.1 126.9 13.4 13.6 0.7

F 5 238.6 1 140.2 626.2 61.7 57.1 12.4 7.3 0.7

M 6 409.9 1 322.4 542.7 50.7 69.8 14.4 6.3 0.6

Russian

Federation

Both 13 546.5 141.7 2 311.2 15.1 145.5 1.5 24.6 0.2

F 6 206.5 133.6 1 167.3 15.6 66.6 1.4 12.5 0.2

M 7 340.0 149.3 1 143.9 14.6 78.8 1.6 12.1 0.2

Rwanda Both 113 210.2 6 507.2 21 136.9 685.5 1 240.6 71.3 253.6 8.2

F 51 158.0 5 903.7 10 069.3 651.2 560.5 64.7 120.8 7.8

M 62 052.2 7 106.1 11 067.5 720.1 680.1 77.9 132.9 8.6

Saint Kitts and

Nevis

Both NA NA NA NA NA NA NA NA



Saint Lucia Both 42.9 391.9 1.6 6.7 0.5 4.3 0.0 0.1

F 20.8 385.2 1.3 11.3 0.2 4.2 0.0 0.1

M 22.1 398.4 0.3 2.3 0.2 4.3 0.0 0.0

157

Saint Vincent and

the Grenadines

Both 68.3 822.7 5.2 28.7 0.7 9.0 0.1 0.3

F 33.1 806.4 2.7 29.6 0.4 8.8 0.0 0.4

M 35.2 838.5 2.6 27.9 0.4 9.1 0.0 0.3

Samoa Both 350.5 1 492.2 48.8 100.7 3.8 16.2 0.6 1.2

F 142.1 1 254.7 20.2 86.3 1.5 13.6 0.2 1.0

M 208.3 1 713.4 28.6 114.2 2.3 18.6 0.3 1.3

San Marino Both NA NA NA NA NA NA NA NA



Sao Tome and

Principe

Both 1 631.9 5 208.9 361.9 658.1 17.9 57.0 4.3 7.8

F 675.4 4 349.9 179.7 658.2 7.4 47.6 2.1 7.8

M 956.5 6 053.0 182.2 658.1 10.5 66.2 2.2 7.8

Saudi Arabia Both 17 303.6 583.4 1 985.3 37.6 189.4 6.4 22.1 0.4

F 8 471.9 579.8 1 106.2 42.6 92.7 6.3 12.4 0.5

M 8 831.7 586.9 879.2 32.8 96.7 6.4 9.7 0.4

Senegal Both 212 158.8 8 338.5 26 683.1 653.7 2 328.6 91.5 320.5 7.9

F 96 393.2 7 682.1 14 105.1 699.2 1 058.7 84.4 169.1 8.4

M 115 765.6 8 977.3 12 577.9 609.2 1 269.9 98.5 151.4 7.3

Serbia Both 719.0 153.3 93.6 9.4 7.6 1.6 0.9 0.1

F 320.8 140.1 40.7 8.4 3.4 1.5 0.4 0.1

M 398.2 165.9 52.9 10.4 4.2 1.8 0.5 0.1

Seychelles Both 30.7 392.2 15.0 116.4 0.3 4.3 0.2 1.4

F 12.4 323.3 7.0 108.1 0.1 3.5 0.1 1.3

M 18.3 458.3 8.1 124.5 0.2 5.0 0.1 1.5

Sierra Leone Both 232 286.8 20 360.9 26 138.2 1 309.4 2 543.8 223.0 311.3 15.6

F 112 502.9 19 785.5 14 266.1 1 425.4 1 232.1 216.7 169.8 17.0

M 119 783.9 20 932.5 11 872.1 1 192.7 1 311.7 229.2 141.5 14.2

158

Singapore Both 213.1 80.3 17.9 3.0 2.3 0.9 0.2 0.0

F 89.0 69.7 10.4 3.6 1.0 0.8 0.1 0.0

M 124.1 90.1 7.5 2.5 1.3 1.0 0.1 0.0

Slovakia Both 575.4 204.5 113.4 20.6 6.2 2.2 1.3 0.2

F 261.3 190.1 62.2 23.1 2.8 2.1 0.7 0.3

M 314.1 218.3 51.2 18.1 3.4 2.4 0.6 0.2

Slovenia Both 14.1 13.2 7.7 3.8 0.1 0.1 0.1 0.0

F 7.1 13.6 1.1 1.1 0.1 0.1 0.0 0.0

M 7.0 12.8 6.6 6.4 0.1 0.1 0.1 0.1

Solomon Islands Both 4 233.0 5 115.5 220.0 144.5 46.2 55.9 2.5 1.7

F 2 074.3 5 174.7 94.5 128.3 22.7 56.5 1.1 1.5

M 2 158.6 5 059.8 125.4 159.7 23.6 55.3 1.5 1.9

Somalia Both 1 068 103.0 40 813.4 62 716.7 1 549.9 11 764.4 449.5 748.1 18.5

F 515 505.8 39 728.6 33 284.4 1 652.7 5 678.8 437.7 396.8 19.7

M 552 597.1 41 880.2 29 432.3 1 448.0 6 085.5 461.2 351.3 17.3

South Africa Both 260 058.2 4 558.5 17 158.8 161.3 2 841.8 49.8 205.1 1.9

F 121 590.9 4 307.4 7 681.2 145.5 1 328.7 47.1 91.4 1.7

M 138 467.4 4 804.5 9 477.6 176.9 1,513.0 52.5 113.7 2.1

South Sudan Both 494 024.8 25 663.0 22 051.9 689.7 5 442.7 282.7 263.2 8.2

F 238 273.8 25 127.4 11 415.7 722.7 2 625.6 276.9 136.1 8.6

M 255 751.0 26 182.9 10 636.2 657.5 2 817.1 288.4 127.0 7.9

Spain Both 235.3 11.4 37.1 0.8 2.5 0.1 0.4 0.0

F 111.5 11.1 24.8 1.1 1.2 0.1 0.3 0.0

M 123.8 11.6 12.2 0.5 1.3 0.1 0.1 0.0

Sri Lanka Both 8 692.4 542.8 2 765.8 80.2 93.8 5.9 31.9 0.9

F 3 597.6 456.8 1 465.1 85.3 38.9 4.9 17.0 1.0

M 5 094.8 626.0 1 300.8 75.1 54.9 6.7 14.9 0.9

Sudan Both 674 268.1 11 350.7 28 973.5 279.9 7 441.0 125.3 343.0 3.3

159

F 383 306.5 13 128.1 14 703.7 288.5 4 230.4 144.9 174.3 3.4

M 290 961.6 9 632.6 14 269.9 271.5 3 210.7 106.3 168.8 3.2

Suriname Both 368.5 732.4 37.9 38.4 4.0 8.0 0.4 0.4

F 158.1 650.8 17.8 37.4 1.7 7.1 0.2 0.4

M 210.4 808.5 20.1 39.3 2.3 8.8 0.2 0.5

Sweden Both 42.0 7.2 16.6 1.5 0.5 0.1 0.2 0.0

F 16.8 5.9 4.8 0.9 0.2 0.1 0.1 0.0

M 25.2 8.4 11.8 2.0 0.3 0.1 0.1 0.0

Switzerland Both 43.3 10.0 8.4 1.0 0.5 0.1 0.1 0.0

F 19.4 9.2 6.7 1.7 0.2 0.1 0.1 0.0

M 23.9 10.7 1.7 0.4 0.3 0.1 0.0 0.0

Syrian Arab

Republic

Both 19 112.1 910.3 4 161.8 86.9 209.3 10.0 49.1 1.0

F 8 512.6 832.4 2 324.0 99.6 93.3 9.1 27.5 1.2

M 10 599.5 984.2 1 837.8 74.9 116.0 10.8 21.6 0.9

Tajikistan Both 73 611.8 6 222.5 5 620.4 297.5 804.3 68.0 65.5 3.5

F 34 720.4 6 028.6 2 835.7 308.5 379.4 65.9 33.0 3.6

M 38 891.4 6 406.4 2 784.7 287.2 424.8 70.0 32.4 3.3

Thailand Both 26 484.1 702.9 4 706.0 56.1 288.4 7.7 55.2 0.7

F 11 180.5 610.3 1 599.6 39.2 121.8 6.6 18.3 0.4

M 15 303.6 790.6 3 106.4 72.1 166.6 8.6 36.9 0.9

The former

Yugoslav

Republic of

Macedonia

Both 814.5 690.4 50.6 21.9 8.8 7.5 0.4 0.2

F 391.5 681.8 5.8 5.2 4.2 7.4 0.0 0.0

M 423.0 698.5 44.8 37.7 4.6 7.5 0.4 0.4

Timor-Leste Both 26 423.1 12 820.9 1 056.6 302.8 289.1 140.3 12.5 3.6

F 13 535.9 13 401.8 594.4 347.6 148.2 146.7 7.0 4.1

M 12 887.2 12 262.6 462.2 259.8 141.0 134.1 5.5 3.1

160

Togo Both 172 757.5 14 691.9 24 074.9 1 200.4 1 898.5 161.5 289.4 14.4

F 75 760.7 12 924.6 14 022.9 1 402.3 832.9 142.1 168.5 16.8

M 96 996.9 16 448.8 10 052.0 999.7 1 065.6 180.7 121.0 12.0

Tonga Both 164.7 1 295.6 21.4 81.9 1.8 14.1 0.2 0.9

F 93.9 1 516.6 8.8 69.6 1.0 16.5 0.1 0.8

M 70.7 1 085.5 12.7 93.3 0.8 11.8 0.1 1.1

Trinidad and

Tobago

Both 482.7 510.4 28.0 14.8 5.2 5.5 0.3 0.2

F 222.5 478.3 7.2 7.8 2.4 5.2 0.1 0.1

M 260.2 541.5 20.7 21.7 2.8 5.9 0.2 0.2

Tunisia Both 5 683.2 540.5 716.4 42.8 62.0 5.9 8.1 0.5

F 2 916.9 567.8 428.5 52.3 31.8 6.2 4.9 0.6

M 2 766.3 514.4 287.9 33.6 30.1 5.6 3.2 0.4

Turkey Both 17 035.1 251.5 2 769.7 20.8 184.7 2.7 31.2 0.2

F 8 288.6 250.6 1 223.3 18.7 89.9 2.7 13.6 0.2

M 8 746.5 252.2 1 546.4 22.7 94.7 2.7 17.5 0.3

Turkmenistan Both 20 070.6 2 829.6 668.3 64.5 219.7 31.0 7.9 0.8

F 8 540.0 2 444.6 285.7 55.8 93.5 26.8 3.4 0.7

M 11 530.5 3 203.2 382.7 73.0 126.2 35.1 4.5 0.9

Tuvalu Both NA NA NA NA NA NA NA NA



Uganda Both 914 692.1 11 880.8 122 668.0 1 004.7 10 044.5 130.5 1 472.5 12.1

F 422 630.1 11 083.4 60 827.1 1 003.6 4 638.1 121.6 729.9 12.0

M 492 062.0 12 663.3 61 840.8 1 005.7 5 406.4 139.1 742.5 12.1

Ukraine Both 6 546.3 280.5 598.4 13.5 70.7 3.0 5.8 0.1

F 3 486.0 308.2 309.1 14.4 37.7 3.3 3.1 0.1

M 3 060.3 254.4 289.3 12.7 33.0 2.7 2.8 0.1

United Arab Both 864.3 186.3 66.0 8.0 9.2 2.0 0.6 0.1

161

Emirates

F 485.9 213.9 22.0 5.5 5.2 2.3 0.2 0.0

M 378.4 159.8 44.0 10.5 4.0 1.7 0.5 0.1

United Kingdom Both 1 201.2 30.0 218.7 2.9 13.1 0.3 2.5 0.0

F 551.8 28.3 98.1 2.6 6.0 0.3 1.1 0.0

M 649.4 31.7 120.7 3.1 7.1 0.3 1.4 0.0

United Republic

of Tanzania

Both 1 041 731.9 10 789.1 114 108.2 741.1 11 439.5 118.5 1 359.2 8.8

F 508 711.1 10 637.3 59 202.8 771.3 5 586.3 116.8 704.8 9.2

M 533 020.8 10 938.0 54 905.4 711.0 5 853.2 120.1 654.4 8.5

United States of

America

Both 4 106.7 20.9 780.7 1.9 44.0 0.2 7.9 0.0

F 1 880.0 19.6 359.9 1.8 20.2 0.2 3.6 0.0

M 2 226.8 22.2 420.8 2.0 23.8 0.2 4.3 0.0

Uruguay Both 170.8 71.3 13.7 2.8 1.8 0.8 0.1 0.0

F 74.6 63.5 4.0 1.6 0.8 0.7 0.0 0.0

M 96.2 78.7 9.8 3.9 1.0 0.9 0.1 0.0

Uzbekistan Both 55 718.4 1 749.8 8 942.2 159.3 607.7 19.1 105.9 1.9

F 24 968.8 1 627.0 4 231.2 154.7 272.3 17.7 50.1 1.8

M 30 749.6 1 864.1 4 711.1 163.7 335.4 20.3 55.8 1.9

Vanuatu Both 1 416.2 4 114.2 104.1 164.5 15.4 44.9 1.2 1.9

F 666.7 4,010.9 39.3 130.0 7.3 43.7 0.5 1.5

M 749.4 4,210.7 64.8 196.1 8.2 45.9 0.8 2.3

Venezuela

(Bolivarian

Republic of)

Both 15 909.9 534.9 1 113.0 19.1 173.7 5.8 13.2 0.2

F 6 898.5 474.6 477.6 16.7 75.3 5.2 5.6 0.2

M 9 011.4 592.6 635.4 21.4 98.4 6.5 7.6 0.3

Viet Nam Both 176 592.3 2 275.5 5 157.4 36.7 1 929.6 24.9 57.7 0.4

F 69 334.7 1 887.1 1 755.6 26.0 757.3 20.6 18.8 0.3

162

M 107 257.6 2 624.6 3 401.9 46.5 1 172.3 28.7 39.0 0.5

Yemen Both 376 758.6 9 245.2 11 148.7 158.6 4 132.0 101.4 130.6 1.9

F 214 536.5 10 763.4 5 987.9 173.9 2 353.2 118.1 70.3 2.0

M 162 222.1 7 791.8 5 160.9 143.9 1 778.8 85.4 60.2 1.7

Zambia Both 309 264.4 10 966.3 29 062.8 623.5 3 394.8 120.4 347.3 7.5

F 147 139.2 10 532.6 14 186.8 612.4 1 615.6 115.7 169.4 7.3

M 162 125.2 11 392.1 14 876.1 634.4 1 779.2 125.0 177.9 7.6

Zimbabwe Both 224 757.2 8 850.7 29 028.2 700.6 2 467.9 97.2 348.2 8.4

F 113 435.3 8 978.0 15 935.1 771.0 1 245.9 98.6 191.2 9.3

M 111 321.9 8 724.7 13 093.1 630.5 1 222.0 95.8 157.0 7.6

NA, not available

F, females; M, males

164

Table 7. Death rate per 100 000 children attributable to the joint effects of household and

ambient air pollution in 2016, by WHO region, the world, and income group, by sex

WHO region Income

group

Death rate per

100 000, boys < 5

years

Death rate per

100 000, girls < 5

years

Death rate per

100 000, boys 5–

14 years

Death rate per

100 000, girls 5–

14 years African LMIC 190.5 177.4 11.6 14.2

HIC 5 3.5 1.5 1.3

Americas LMIC 15.2 13.2 0.7 0.6

HIC 0.3 0.3 0 0

Eastern

Mediterranean

LMIC 94.6 102.8 3.7 3.5

HIC 5.2 5.4 0.3 0.4

European LMIC 9.1 8.4 0.6 0.5

HIC 0.3 0.2 0 0

South-East Asia LMIC 68.8 81.8 2.1 2.9

HIC NA NA NA NA

Western Pacific LMIC 21.4 19.5 1 1.1

HIC 0.4 0.3 0 0

All LMIC 87.6 89.9 4.1 5

HIC 0.7 0.6 0 0.1

World 79.6 81.6 3.7 4.5

LMIC, low- and middle-in come country; HIC, high-income country

NA, not available

Table 8. Population attributable fraction (PAF) of childhood mortality due to ambient air

pollution, by WHO region and income level, 2016

WHO region Income level Children < 5 years

(%)

Children 5–14

years (%)

African LMIC 28 29

HIC 17 17

Americas LMIC 15 16

HIC 8 7

Eastern Mediterranean LMIC 32 33

HIC 37 38

European LMIC 20 20

HIC 12 13


HIC NA NA


HIC 11 11

All LMIC 30 29

HIC 17 15

World 30 29


NA, not available

165

Table 9. Population attributable fraction (PAF) of childhood mortality due to household air

pollution, by WHO region and income level, 2016

WHO region Income level

Children < 5 years

(%)

Children 5–14

years (%)

African LMIC 53 52

HIC 9 9

Americas LMIC 23 22

HIC 1 0

Eastern Mediterranean LMIC 38 33

HIC 4 4

European LMIC 9 8

HIC 1 0


HIC NA NA


HIC 0 0

All LMIC

HIC

46

2

46

1

World 46 46


NA, not available

Fig. 15. Disability-adjusted life years (DALYs) per 100 000 capita due to acute lower

respiratory infections associated with the joint effects of ambient and household air pollution in

children under 5 years of age in 2016

Source: see Annex 2

166

Fig. 16. Disability-adjusted life years (DALYs) rate per 100 000 due to acute lower respiratory

infections associated with the joint effects of ambient and household air pollution in children 5–

14 years of age in 2016

Source: see Annex 2